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Li, Wantao. "Linear Regression with Regularized Hyperparameter Optimization." Theoretical and Natural Science 105, no. 1 (2025): 106–12. https://doi.org/10.54254/2753-8818/2025.22920.
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Traditional linear regression struggles with noisy data due to its lack of regularization, which fails to mitigate overfitting effectively. This results in excellent fitting on training data but poor generalization on test sets. Additionally, its reliance on a single analytical solution via the normal equation limits adaptability to complex, dynamic data relationships, particularly under significant noise interference. To address these shortcomings, this paper introduces a linear regression method with regularized hyperparameter optimization (Reg-LR). Standard linear regression uses the normal equation to efficiently capture basic linear patterns, while Reg-LR incorporates hyperparameters to dynamically regulate model complexity, balancing fitting accuracy and generalization. By optimizing the loss function, this approach enhances performance from basic fitting to robust prediction. Experiments feature two key modules: a data generation module using NumPy to produce simulated datasets with Gaussian noise, simulating realistic conditions, and a regularization optimization module employing gradient descent to tune parameters across various hyperparameter values. Results indicate that standard linear regression achieves a test set mean squared error (MSE) of 3.90, while Reg-LR optimizes it to 6.56 through tuning. Though improvements are modest on small datasets, Reg-LR demonstrates robustness in noisy environments. Ablation studies highlight the regularization terms role in preventing overfitting and the impact of hyperparameter choices on model stability. This method provides a scalable tuning framework for linear regression and a foundation for complex predictive tasks, offering theoretical and practical significance.
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Soraya, Nadia Sofie, and Hendry Hendry. "Komparasi linear regression, random forest regression, dan multilayer perceptron regression untuk prediksi tren musik TikTok." AITI 20, no. 2 (2023): 191–205. http://dx.doi.org/10.24246/aiti.v20i2.191-205.
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Prediksi korelasi feature audio terhadap lagu yang populer di TikTok merupakan hal penting dalam industri musik. Dengan bekal data yang memiliki beberapa feature audio maka dilakukan penelitian menggunakan metode Linear Regression (LR), Random Forest Regression (RFR), dan Multilayer Perceptron Regression (MLP Regression) untuk membandingkan model yang dapat memprediksi popularitas secara efektif dan feature yang mempengaruhi popularitas lagu di TikTok. Selain itu dilakukan juga Exploratory Data Analysis (EDA) untuk mendapatkan insight data. Hasil dari proses EDA yaitu popularitas lagu terbanyak berada pada range 40-80, durasi lagu antara 2-3 menit, feature loudness berkorelasi positif dengan energy, demikian juga antara artist_pop dan track_pop. Set feature importance pada model LR dan RFR untuk feature target track_pop adalah artist_pop, loudness, dan duration_ms. Metode LR memiliki hasil terbaik untuk dataset yang dipakai, dengan MSE sebesar 0.0313, RMSE sebesar 0.177, dan MAE sebesar 0.118.
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Pawar, Priyanka, Sanika Gonjari, Sharwari Kshirsagar, and Dr G. J. Chhajed. "A Review of Linear Regression and Support Vector Regression." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem40400.
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Predicting the future of a business requires analyzing key insights such as consumer behavior, product performance, and profitability using current and historical data. Statistical techniques play a vital role in generating these insights and enabling accurate forecasting, particularly with time series data. Applications like weather forecasting, financial markets, and stock analysis often integrate historical and real-time data for better accuracy. Regression models, including linear regression and support vector regression (SVR), are commonly employed to analyze time series data. Linear Regression (LR) and Support Vector Regression (SVR) are two widely used machine learning algorithms for predicting continuous outcomes. LR is a parametric method that models the relationship between independent and dependent variables by fitting a linear equation, assuming a linear dependency. It is simple, interpretable, and efficient for linearly separable data. SVR, a non-parametric technique derived from Support Vector Machines, employs kernel functions to handle non-linear relationships and constructs a decision boundary by maximizing the margin within an error tolerance (epsilon). SVR is robust to outliers and effective for complex, high-dimensional data.
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Basha, D. Khalandar, and T. Venkateswarlu. "Linear Regression Supporting Vector Machine and Hybrid LOG Filter-Based Image Restoration." Journal of Intelligent Systems 29, no. 1 (2019): 1480–95. http://dx.doi.org/10.1515/jisys-2018-0492.
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Abstract The image restoration (IR) technique is a part of image processing to improve the quality of an image that is affected by noise and blur. Thus, IR is required to attain a better quality of image. In this paper, IR is performed using linear regression-based support vector machine (LR-SVM). This LR-SVM has two steps: training and testing. The training and testing stages have a distinct windowing process for extracting blocks from the images. The LR-SVM is trained through a block-by-block training sequence. The extracted block-by-block values of images are used to enhance the classification process of IR. In training, the imperfections on the image are easily identified by setting the target vectors as the original images. Then, the noisy image is given at LR-SVM testing, based on the original image restored from the dictionary. Finally, the image block from the testing stage is enhanced using the hybrid Laplacian of Gaussian (HLOG) filter. The denoising of the HLOG filter provides enhanced results by using block-by-block values. This proposed approach is named as LR-SVM-HLOG. A dataset used in this LR-SVM-HLOG method is the Berkeley Segmentation Database. The performance of LR-SVM-HLOG was analyzed as peak signal-to-noise ratio (PSNR) and structural similarity index. The PSNR values of the house and pepper image (color image) are 40.82 and 36.56 dB, respectively, which are higher compared to the inter- and intra-block sparse estimation method and block matching and three-dimensional filtering for color images at 20% noise.
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Zhang, Liye, Xiaoliang Meng, and Chao Fang. "Linear Regression Algorithm against Device Diversity for the WLAN Indoor Localization System." Wireless Communications and Mobile Computing 2021 (April 8, 2021): 1–15. http://dx.doi.org/10.1155/2021/5530396.
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Recent years have witnessed a growing interest in using WLAN fingerprint-based methods for the indoor localization system because of their cost-effectiveness and availability compared to other localization systems. In this system, the received signal strength (RSS) values are measured as the fingerprint from the access points (AP) at each reference point (RP) in the offline phase. However, signal strength variations across diverse devices become a major problem in this system, especially in the crowdsourcing-based localization system. In this paper, the device diversity problem and the adverse effects caused by this problem are analyzed firstly. Then, the intrinsic relationship between different RSS values collected by different devices is mined by the linear regression (LR) algorithm. Based on the analysis, the LR algorithm is proposed to create a unique radio map in the offline phase and precisely estimate the user’s location in the online phase. After applying the LR algorithm in the crowdsourcing systems, the device diversity problem is solved effectively. Finally, we verify the LR algorithm using the theoretical study of the probability of error detection. Experimental results in a typical office building show that the proposed method results in a higher reliability and localization accuracy.
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Meng, Lianxiao, Lin Yang, Shuangyin Ren, et al. "An Approach of Linear Regression-Based UAV GPS Spoofing Detection." Wireless Communications and Mobile Computing 2021 (May 7, 2021): 1–16. http://dx.doi.org/10.1155/2021/5517500.
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A prominent security threat to unmanned aerial vehicle (UAV) is to capture it by GPS spoofing, in which the attacker manipulates the GPS signal of the UAV to capture it. This paper introduces an anti-spoofing model to mitigate the impact of GPS spoofing attack on UAV mission security. In this model, linear regression (LR) is used to predict and model the optimal route of UAV to its destination. On this basis, a countermeasure mechanism is proposed to reduce the impact of GPS spoofing attack. Confrontation is based on the progressive detection mechanism of the model. In order to better ensure the flight security of UAV, the model provides more than one detection scheme for spoofing signal to improve the sensitivity of UAV to deception signal detection. For better proving the proposed LR anti-spoofing model, a dynamic Stackelberg game is formulated to simulate the interaction between GPS spoofer and UAV. In particular, for GPS spoofer, it is worth mentioning that for the scenario that the UAV is cheated by GPS spoofing signal in the mission environment of the designated route is simulated in the experiment. In particular, UAV with the LR anti-spoofing model, as the leader in this game, dynamically adjusts its response strategy according to the deception’s attack strategy when upon detection of GPS spoofer’s attack. The simulation results show that the method can effectively enhance the ability of UAV to resist GPS spoofing without increasing the hardware cost of the UAV and is easy to implement. Furthermore, we also try to use long short-term memory (LSTM) network in the trajectory prediction module of the model. The experimental results show that the LR anti-spoofing model proposed is far better than that of LSTM in terms of prediction accuracy.
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Siva, Ramgiri, and Adimoolam M. "Linear Regression Algorithm Based Price Prediction of Car and Accuracy Comparison with Support Vector Machine Algorithm." ECS Transactions 107, no. 1 (2022): 12953–64. http://dx.doi.org/10.1149/10701.12953ecst.
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The aim of the study is to use Linear Regression (LR) algorithm based price prediction of car price and accuracy comparison with support vector machine (SVM) classification algorithm. Materials and methods: LR (N=205) and SVM algorithm (N=205) are applied for car price prediction as a mechanism. The accuracy and prediction of the classifiers was evaluated and recorded with G power 80% and alpha value 0.05. Results: The SVM produces 89% accuracy in predicting the car price on the sample dataset and the LR predicts the accuracy at the rate 91.7%. LR algorithm based accuracy appears (significant 0.563) to be better than SVM algorithm for car price prediction. Conclusion: The accuracy performance parameter of the LR algorithm appears to be better than the SVM algorithm.
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Lu, Bing, and Yuhong He. "Evaluating Empirical Regression, Machine Learning, and Radiative Transfer Modelling for Estimating Vegetation Chlorophyll Content Using Bi-Seasonal Hyperspectral Images." Remote Sensing 11, no. 17 (2019): 1979. http://dx.doi.org/10.3390/rs11171979.
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Different types of methods have been developed to retrieve vegetation attributes from remote sensing data, including conventional empirical regressions (i.e., linear regression (LR)), advanced empirical regressions (e.g., multivariable linear regression (MLR), partial least square regression (PLSR)), machine learning (e.g., random forest regression (RFR), decision tree regression (DTR)), and radiative transfer modelling (RTM, e.g., PROSAIL). Given that each algorithm has its own strengths and weaknesses, it is essential to compare them and evaluate their effectiveness. Previous studies have mainly used single-date multispectral imagery or ground-based hyperspectral reflectance data for evaluating the models, while multi-seasonal hyperspectral images have been rarely used. Extensive spectral and spatial information in hyperspectral images, as well as temporal variations of landscapes, potentially influence the model performance. In this research, LR, PLSR, RFR, and PROSAIL, representing different types of methods, were evaluated for estimating vegetation chlorophyll content from bi-seasonal hyperspectral images (i.e., a middle- and a late-growing season image, respectively). Results show that the PLSR and RFR generally performed better than LR and PROSAIL. RFR achieved the highest accuracy for both images. This research provides insights on the effectiveness of different models for estimating vegetation chlorophyll content using hyperspectral images, aiming to support future vegetation monitoring research.
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Peng, Yali, Lu Zhang, Shigang Liu, Xili Wang та Min Guo. "Kernel Negative ε Dragging Linear Regression for Pattern Classification". Complexity 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/2691474.
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Linear regression (LR) and its variants have been widely used for classification problems. However, they usually predefine a strict binary label matrix which has no freedom to fit the samples. In addition, they cannot deal with complex real-world applications such as the case of face recognition where samples may not be linearly separable owing to varying poses, expressions, and illumination conditions. Therefore, in this paper, we propose the kernel negative ε dragging linear regression (KNDLR) method for robust classification on noised and nonlinear data. First, a technique called negative ε dragging is introduced for relaxing class labels and is integrated into the LR model for classification to properly treat the class margin of conventional linear regressions for obtaining robust result. Then, the data is implicitly mapped into a high dimensional kernel space by using the nonlinear mapping determined by a kernel function to make the data more linearly separable. Finally, our obtained KNDLR method is able to partially alleviate the problem of overfitting and can perform classification well for noised and deformable data. Experimental results show that the KNDLR classification algorithm obtains greater generalization performance and leads to better robust classification decision.
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Bode, Andi. "PERBANDINGAN METODE PREDIKSI SUPPORT VECTOR MACHINE DAN LINEAR REGRESSION MENGGUNAKAN BACKWARD ELIMINATION PADA PRODUKSI MINYAK KELAPA." Simtek : jurnal sistem informasi dan teknik komputer 4, no. 2 (2019): 104–7. http://dx.doi.org/10.51876/simtek.v4i2.57.
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Pohon kelapa banyak dimanfaatkan oleh manusia, sehingga tumbuhan ini dianggap tumbuhan serbaguna, salah satunya minyak kelapa yang dihasilkan oleh buah pohon kelapa. Produksi jumlah minyak kelapa menjadi bagian penting disetiap perusahaan yang bergerak di bidang produksi dengan tujuan mencapai target hasil produksi. Namaun Produksi minyak setiap hari mengalami perubahan fluktuatif. Perusahaan sangat memerlukan prediksi jumlah produksi. Penelitian ini bermaksud membandingakn metode support vector machine dan linear regression mengunakan fitur seleksi backward elimination berdasarkan data time series Sales Order. Hasil penelitian pada dataset sales order dengan menggunakan metode Support Vector Machine (SVM) didapatkan RMSE 0,127, dengan menggunakan metode SVM dan Backward Elimination (BE) didapatkan RMSE 0,115, dengan metode Linear Regression (LR) didapatkan RMSE 0,118 dan dengan menggunakan metode LR dan Backward Elimination didapatkan RMSE 0,118. Dari hasil perbandingan tersebut dapat disimpulkan bahwa kinerja SVM menggunakan Backward Elimination lebih baik dibanding SVM, LR dan LR menggunakan Backward Elimination
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McCloskey, PJ, and Rodrigo Malheiros Remor. "Comparative Analysis of ARIMA, VAR, and Linear Regression Models for UAE GDP Forecasting." Emirati Journal of Business, Economics, & Social Studies 4, no. 1 (2025): 23–33. https://doi.org/10.54878/gh57cx16.
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Forecasting GDP is crucial for economic planning and policymaking. This study compares the performance of three widely-used econometric models—ARIMA, VAR, and Linear Regression—using GDP data from the UAE. Employing a rolling forecast approach, we analyze the models’ accuracy over different time horizons. Results indicate ARIMA’s robust long-term forecasting capability, LR models perform better with short-term predictions, particularly when exogenous variable forecasts are accurate. These insights provide a valuable foundation for selecting forecasting models in the UAE’s evolving economy, suggesting ARIMA’s suitability for long-term outlooks and LR for short-term, scenario-based forecasts.
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Marie-Sainte, Souad Larabi, Tanzila Saba, and Sihaam Alotaibi. "Air Passenger Demand Forecasting Using Particle Swarm Optimization and Firefly Algorithm." Journal of Computational and Theoretical Nanoscience 16, no. 9 (2019): 3735–43. http://dx.doi.org/10.1166/jctn.2019.8242.
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Air travel demand is a crucial part of planning for airlines and airports. It helps in elaborating decisions and recognizing risks and opportunities. Forecasting air passenger demand is an interesting research study that deserves investigation. This problem requires prediction techniques such that Linear Regression and Neural Network. These techniques are efficient, but they have several parameters that necessitate appropriate values to provide the least error rate of prediction. Some recent air travel demand studies investigated Genetic Algorithms to provide optimal values for these parameters. In this article, we propose to explore the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) to find the optimal values for Linear Regression (LR) coefficients. This study presents two new hybrid prediction techniques (PSO based LR and FA based LR) to handle airline demand forecasting, which researchers have not previously covered. The results of PSO based LR, FA-based LR and LR are compared to find the best model with the lowest prediction error rate. The results showed that PSO based LR achieved the best prediction results with a lower error rate compared to FA based LR and LR alone. This study is performed using the data of Los Angeles International airport.
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Abbas, Mohd Azwan, Norshahrizan Mohd Hashim, Mohamad Faiz Mohd Zaim, et al. "DIRECTIONS OF LINE CONSTRAINTS DILEMMA IN PARAMETRIC LINEAR REGRESSION." Journal of Southwest Jiaotong University 56, no. 5 (2021): 552–62. http://dx.doi.org/10.35741/issn.0258-2724.56.5.50.
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The demand for positional accuracy and multi-dimensional data have demonstrated drastic changes in the geomatics data adjustment approach. Furthermore, the capability of modern sensors to provide high accuracy data (i.e., global navigation satellite system) has caused the crucial requirement for a rigorous adjustment that can process data from multi-sensors. Geomatics practitioners have gradually transformed the adjustment procedure to the most rigorous approach (i.e., parametric linear regression) to adapt to current demand. However, legacy datasets that utilize independent line constraint in the traditional adjustment approach have caused significant uncertainties in parametric linear regression (LR) adjustment. To resolve this dilemma, this research has designed robust experiments using closed traverse types: single-line constraint, multi-line constraints, and sub-network line constraint. Through errors trend and network form deterioration analyses, the outcomes have visually and numerically verified the insignificant of independent line constraints in parametric LR. However, the establishment of control points at the beginning or end of lines could solve the limitation of the abovementioned issue. In both analyses, control points at initial lines have demonstrated the best solution for constrained adjustment. The obtained results have exemplified the appropriate implementation of network adjustment in the presence of line constraints. As positional accuracy becomes the main priority, it can be concluded that points-based constraints are more advisable in preserving the quality of cadastral network adjustment.
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Zhu, Liangji. "Optimization of linear regression in house price prediction." Applied and Computational Engineering 6, no. 1 (2023): 805–12. http://dx.doi.org/10.54254/2755-2721/6/20230928.
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House price prediction plays a very important role in housing transactions. Linear regression based algorithms show good effects in predicting house prices. They have strong interpretability and fast operation speed. However, people ignore the estimation of deviations in linear regression (LR) algorithms. In this paper, k-nearest neighbor (KNN) algorithm is supposed to estimate deviations that are added to the result of linear regression to predict house prices accurately. Furthermore, deviation regression (DR) algorithm is supposed to make the prediction result more accurate. By utilizing Boston House Price data from Kaggle, extensive experiments are conducted and demonstrate the superior performance and compatibility of DR.
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Chang, Ying-Hsin, Jun-Yan Chen, Chiou-Yi Hor, Yu-Chung Chuang, Chang-Biau Yang, and Chia-Ning Yang. "Computational Study of Estrogen Receptor-Alpha Antagonist with Three-Dimensional Quantitative Structure-Activity Relationship, Support Vector Regression, and Linear Regression Methods." International Journal of Medicinal Chemistry 2013 (May 14, 2013): 1–13. http://dx.doi.org/10.1155/2013/743139.
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Human estrogen receptor (ER) isoforms, ERα and ERβ, have long been an important focus in the field of biology. To better understand the structural features associated with the binding of ERα ligands to ERα and modulate their function, several QSAR models, including CoMFA, CoMSIA, SVR, and LR methods, have been employed to predict the inhibitory activity of 68 raloxifene derivatives. In the SVR and LR modeling, 11 descriptors were selected through feature ranking and sequential feature addition/deletion to generate equations to predict the inhibitory activity toward ERα. Among four descriptors that constantly appear in various generated equations, two agree with CoMFA and CoMSIA steric fields and another two can be correlated to a calculated electrostatic potential of ERα.
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Kumar, P. V. S., and N. S. Kumar. "Analysis and Comparison for Prediction of Diabetic among Pregnant Women using Innovative K-Nearest Neighbor Algorithm over Logistic Regression with Improved Accuracy." CARDIOMETRY, no. 25 (February 14, 2023): 949–55. http://dx.doi.org/10.18137/cardiometry.2022.25.949955.
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Aim: In comparison to the K-Nearest Neighbor (KNN) algorithm, Logistic Regression (LR) was used in machine learning algorithms for the prediction of diabetes among pregnant women to get better accuracy, PEARCEsensitivity, and precision. Materials and methods: To verify the usefulness of the technique, researchers collected data sets from free available data sets such as the Pima Indian dataset from the UCI website to examine diabetes among pregnant women. There are two groups in this study: K-Nearest Neighbor (N=20) and Logistic Regression (N=20), each having a sample size of 40. A pre-test power of 80%, a threshold of 0.05, and a confidence interval of 95% are used in the sample size calculation. Results: The accuracy, sensitivity, and precision of algorithms are used to evaluate their performance. K-Nearest Neighbor (KNN) accuracy rate is 72.44 percent, but Linear Regression (LR) accuracy is 76.67%. The sensitivity rate for K-Nearest Neighbor is 74.42 percent, while the sensitivity rate for Linear Regression (LR) is 76.16 percent. The precision rate for K-Nearest Neighbor (KNN) is 73.75percent, whereas the precision rate for Linear Regression (LR) is 81.87 percent. The accuracy rate is significantly different P=0.366 (P>0.05). Conclusion: When compared to the Innovative K-Nearest Neighbor algorithm, the Logistic Regression algorithm predicts better classification in discovering the accuracy, sensitivity, and precision for accessing the rate for prediction of diabetes among pregnant women.
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Aggarwal, Shiva. "Intelligence VM Allocation and Selection Policy based on Local Linear Regression (LR)." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (2018): 2748–54. http://dx.doi.org/10.22214/ijraset.2018.4460.
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Coppi, Renato, Pierpaolo D’Urso, Paolo Giordani, and Adriana Santoro. "Least squares estimation of a linear regression model with LR fuzzy response." Computational Statistics & Data Analysis 51, no. 1 (2006): 267–86. http://dx.doi.org/10.1016/j.csda.2006.04.036.
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Terblanche, Jacques, Johan van der Merwe, and Ryno Laubscher. "Linear and Non-Linear Regression Methods for the Prediction of Lower Facial Measurements from Upper Facial Measurements." Mathematical and Computational Applications 29, no. 4 (2024): 61. http://dx.doi.org/10.3390/mca29040061.
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Accurate assessment and prediction of mandible shape are fundamental prerequisites for successful orthognathic surgery. Previous studies have predominantly used linear models to predict lower facial structures from facial landmarks or measurements; the prediction errors for this did not meet clinical tolerances. This paper compared non-linear models, namely a Multilayer Perceptron (MLP), a Mixture Density Network (MDN), and a Random Forest (RF) model, with a Linear Regression (LR) model in an attempt to improve prediction accuracy. The models were fitted to a dataset of measurements from 155 subjects. The test-set mean absolute errors (MAEs) for distance-based target features for the MLP, MDN, RF, and LR models were respectively 2.77 mm, 2.79 mm, 2.95 mm, and 2.91 mm. Similarly, the MAEs for angle-based features were 3.09°, 3.11°, 3.07°, and 3.12° for each model, respectively. All models had comparable performance, with neural network-based methods having marginally fewer errors outside of clinical specifications. Therefore, while non-linear methods have the potential to outperform linear models in the prediction of lower facial measurements from upper facial measurements, current results suggest that further refinement is necessary prior to clinical use.
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Shayan, Zahra, Naser Mohammad Gholi Mezerji, Leila Shayan, and Parisa Naseri. "Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression." Global Journal of Health Science 8, no. 7 (2015): 41. http://dx.doi.org/10.5539/gjhs.v8n7p41.
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<p><strong>BACKGROUND: </strong>Logistic regression (LR) and linear discriminant analysis (LDA) are two popular<strong> </strong>statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices.</p><p><strong>METHODS:</strong> This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models.</p><p><strong>RESULTS:</strong> CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model.</p><p><strong>CONCLUSION:</strong> The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.</p>
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Acar, Tülin. "Determination of a Differential Item Functioning Procedure Using the Hierarchical Generalized Linear Model." SAGE Open 2, no. 1 (2012): 215824401243676. http://dx.doi.org/10.1177/2158244012436760.
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The aim of this research is to compare the result of the differential item functioning (DIF) determining with hierarchical generalized linear model (HGLM) technique and the results of the DIF determining with logistic regression (LR) and item response theory–likelihood ratio (IRT-LR) techniques on the test items. For this reason, first in this research, it is determined whether the students encounter DIF with HGLM, LR, and IRT-LR techniques according to socioeconomic status (SES), in the Turkish, Social Sciences, and Science subtest items of the Secondary School Institutions Examination. When inspecting the correlations among the techniques in terms of determining the items having DIF, it was discovered that there was significant correlation between the results of IRT-LR and LR techniques in all subtests; merely in Science subtest, the results of the correlation between HGLM and IRT-LR techniques were found significant. DIF applications can be made on test items with other DIF analysis techniques that were not taken to the scope of this research. The analysis results, which were determined by using the DIF techniques in different sample sizes, can be compared.
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Nguyen, Thi Bich Ngoc, and Prabhu Paramasivam. "Model-prediction of Efficiency of a Parabolic Trough Collector Using Data-Driven Soft Computing." International Journal on Computational Engineering 1, no. 1 (2024): 1–8. http://dx.doi.org/10.62527/comien.1.1.6.
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The study highlights the need to use suitable modeling techniques to accurately predict the efficiency of parabolic trough collectors in light of their significance in renewable energy. An examination of prediction models using the Linear Regression (LR), Support Vector Regression (SVR), and Decision Tree (DT) algorithms for the efficiency of parabolic trough collectors provides insightful information about how well they work. In terms of prediction accuracy and precision, the Decision Tree model regularly performs better than its rivals throughout the training and testing phases. What sets it apart from SVR and LR models is its ability to identify minute relationships within the data. SVR performs better than LR, although it is not as exact or accurate as the DT model. Among the three models, Linear Regression has the lowest performance, underscoring its limitations in terms of capturing non-linear relationships. Given its exceptional performance, the Decision Tree model may prove to be a crucial instrument in encouraging the design and construction of solar energy systems, hence advancing the growth of sustainable development projects and renewable energy technology.
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Alam, MJ, M. Alamin, MR Hossain, SMS Islam, and MNH Mollah. "Robust linear regression based simple interval mapping for QTL analysis with backcross population." Journal of Bio-Science 24 (July 18, 2018): 75–81. http://dx.doi.org/10.3329/jbs.v24i0.37489.
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Simple interval mapping (SIM) is one of the most important techniques for the identification of quantitative trait locus (QTL). Most of the approaches of SIM are very sensitive to phenotypic outliers and produce misleading results. There is a robust approach of SIM only for F2 population. However, there is no robust SIM method for Backcross population. The objective was to develop a new approach of SIM with Backcross population which is robust against phenotypic outliers and performs almost the same as existing classical methods in absence of outliers. Maximum likelihood (ML) and linear regression (LR) based approaches of SIM are not robust against phenotypic outliers. In this research, we have developed a robust regression based SIM approach by maximizing β-likelihood function for Backcross population. The proposed method reduces to the LR-based SIM method when β = 0. To measure the performance of the proposed method in comparison of ML and LR based SIM with backcross population; we have generated phenotypic and genotypic data for Backcross population using simulation technique. LOD score profile plot shows that the highest peaks of LOD scores occur in the true QTL positions of the true chromosomes at true markers by all three methods for the uncontaminated dataset. However, in presence of outliers, only the proposed method gives the highest LOD score peaks at the true QTL positions on the true chromosomes. The simulation results showed that the proposed method improves performance over the existing SIM methods in presence of phenotypic contaminations.J. bio-sci. 24: 75-81, 2016
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Mohamed, Mona. "Intelligent Fat Predictor: Leveraging Linear Regression and K Nearest Neighbors in Obesity diseases." International Journal of Advances in Applied Computational Intelligence 3, no. 1 (2023): 08–18. http://dx.doi.org/10.54216/ijaaci.030101.
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One of the major lifestyle disorders brought on by unwholesome daily routines and inherited ailments is obesity and overweight. And this illness is a risk factor for a wide range of chronic illnesses, such as cancer, diabetes, metabolic syndrome, and cardiovascular conditions. Additionally, according to the World Health Organization (WHO), 30% of deaths worldwide will be caused by lifestyle illnesses by 2030. These deaths can be prevented by appropriately identifying and treating risk factors that relate to these diseases as well as by implementing behavioral engagement policies. Thence, the study is leveraging machine learning (ML) techniques for analyzing data and discovering new patterns for predicting body fat. The problem of predicting fat classifies as a regression, hence, we are deploying two regression techniques to deal with the regression dataset. These techniques are used linear regression (LR) and k nearest neighbors (KNN) which fall under umbral of ML. The two techniques are applied on real datasets. The dataset has 252 records. The results showed the LR has the highest score than the KNN model.
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Subramanian, Ananda Kumar, Aritra Samanta, Sasmithaa Manickam, Abhinav Kumar, Stavros Shiaeles, and Anand Mahendran. "Linear Regression Trust Management System for IoT Systems." Cybernetics and Information Technologies 21, no. 4 (2021): 15–27. http://dx.doi.org/10.2478/cait-2021-0040.
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Abstract This paper aims at creating a new Trust Management System (TMS) for a system of nodes. Various systems already exist which only use a simple function to calculate the trust value of a node. In the age of artificial intelligence the need for learning ability in an Internet of Things (IoT) system arises. Malicious nodes are a recurring issue and there still has not been a fully effective way to detect them beforehand. In IoT systems, a malicious node is detected after a transaction has occurred with the node. To this end, this paper explores how Artificial Intelligence (AI), and specifically Linear Regression (LR), could be utilised to predict a malicious node in order to minimise the damage in the IoT ecosystem. Moreover, the paper compares Linear regression over other AI-based TMS, showing the efficiency and efficacy of the method to predict and identify a malicious node.
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Asyari, Fajar Husain, Ellen Proborini, Melina Dwi Safitri, and Eko Hari Rachmawanto. "Comparison of Shallot Price Prediction In Pati City With LSTM, GRU and Linear Regression." Journal of Applied Intelligent System 9, no. 2 (2024): 282–92. https://doi.org/10.62411/jais.v9i2.11373.
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Shallots are superior vegetable plant and contribute quite significantly to the development of the national economy. The price of shallots fluctuates almost every year. At certain times the price of shallots soars due to high demand while the supply in the market is insufficient. Therefore, an analysis is needed to see what phenomena significantly affect the increase in the price of shallots. The methods used in the study were LSTM, GRU and LR. The results of the analysis show that the LSTM algorithm gets a MAE value of 0.011072172783, MAPE 3.93678% and RMSE 0.03139695060, this error is the lowest compared to GRU getting MAE value is 0.01185741, MAPE 4.2282357% and RMSE 0.03122299395 and LR with MAE 0.0134737280395416, MAPE 5.45081% and RMSE is 0.0313332635305961, so LSTM is a suitable algorithm for predicting shallot data in Pati district.
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Carlos, Hernandez, Lagos Dafne, Leal Paola, and Castillo Jaime. "Emergency patient forecasting with models based on support vector machines." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3129–40. https://doi.org/10.11591/ijai.v13.i3.pp3129-3140.
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Understanding the dynamic nature of the influx of patients is crucial for efficiently managing supplies, medical personnel, and infrastructure in an emergency room (ER). While overestimation can lead to resource wastage, underestimation can result in shortages and compromised service quality. This study addresses emergency patient forecast by means of implementing support vector machine (SVM) algorithms. Along four phases (analysis, design, development, and validation), more than 50,000 ER records were preprocessed and analyzed. Traditional error metrics such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were utilized alongside monthly consolidated forecasts. To benchmark performance, actual values and forecasts derived from linear regression (LR) models were used. Experiments revealed that LR models had lower errors compared to SVM models. However, monthly consolidated forecasts showed that SVM-based models underestimated less than LR-based models. In conclusion, SVM-based models could help planners to accurately estimate the requirements for supplies and medical personnel during the period under study.
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Salehpour, Arash. "Predicting Automobile Stock Prices Index in the Tehran Stock Exchange Using Machine Learning Models." International Journal of Intelligent Systems and Applications 15, no. 5 (2023): 12–17. http://dx.doi.org/10.5815/ijisa.2023.05.02.
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This paper analyses the performance of machine learning models in forecasting the Tehran Stock Exchange's automobile index. Historical daily data from 2018-2022 was pre-processed and used to train Linear Regression (LR), Support Vector Regression (SVR), and Random Forest (RF) models. The models were evaluated on mean absolute error, mean squared error, root mean squared error and R2 score metrics. The results indicate that LR and SVR outperformed RF in predicting automobile stock prices, with LR achieving the lowest error scores. This demonstrates the capability of machine learning techniques to model complex, nonlinear relationships in financial time series data. This pioneering study on a previously unexplored dataset provides empirical evidence that LR and SVR can reliably forecast automobile stock market prices, holding promise for investing applications.
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Olyasani, Mojtaba, Hamed Azimi, and Hodjat Shiri. "Determining Non-Dimensional Group of Parameters Governing the Prediction of Penetration Depth and Holding Capacity of Drag Embedment Anchors Using Linear Regression." Journal of Marine Science and Engineering 13, no. 7 (2025): 1332. https://doi.org/10.3390/jmse13071332.
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Drag embedment anchors (DEAs) provide reliable and cost-effective mooring solutions for floating structures, e.g., platforms, ships, offshore wind turbines, etc., in offshore engineering. Structural stability and operational safety require accurate predictions of their penetration depths and holding capacities across various seabed conditions. In this study, explicit linear regression (LR) models were developed for the first time to predict the penetration depth and holding capacity of DEAs on clay and sand seabed. Buckingham’s theorem was also applied to identify dimensionless groups of parameters that influence DEA behavior, e.g., the penetration depth and holding capacity of the DEAs. LR models were developed and validated against experimental data from the literature for both clay and sand seabed. To evaluate model performance and identify the most accurate LR models to predict DEA behavior, comprehensive sensitivity, error, and uncertainty analyses were performed. Additionally, LR analysis revealed the most influential input parameters impacting penetration depth and holding capacity. Regarding offshore mooring design and geotechnical engineering applications, the proposed LR models offered a practical and efficient approach to estimating DEA performance across various seabed conditions.
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Tantawy, Alshaimaa A. "Linear Regression and K Nearest Neighbors Machine Learning Models for Person Fat Forecasting." International Journal of Advances in Applied Computational Intelligence 3, no. 2 (2023): 38–47. http://dx.doi.org/10.54216/ijaaci.030204.
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Predicting a person's person fat percentage is an important part of keeping tabs on their health and fitness. An accurate assessment of person fat allows for the development of individualized programmer for health and wellbeing, the promotion of illness prevention, and the evaluation of the efficacy of weight management initiatives. This study reviews the current state of the art in person fat prediction approaches, which includes the use of machine learning algorithms. Obesity is a chronic condition characterized by high levels of person fat and is linked to several health issues. Since several methods exist for estimating person fat percentage to evaluate obesity, these assessments are usually expensive and need specialized equipment. Therefore, determining obesity and its associated disorders requires an accurate estimate of person fat proportion according to readily available person measures. This paper presented a machine-learning model for forecasting person fat. This problem is a regression, so this paper used two regression models to deal with the regression dataset. This paper used linear regression (LR) and k nearest neighbors (KNN). The two models were applied to real datasets. The dataset has 252 records. The results showed the LR has the highest score than the KNN model.
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Siniksaran, Enis. "On the geometry ofF, Wald, LR, and LM tests in linear regression models." Statistics 39, no. 4 (2005): 287–99. http://dx.doi.org/10.1080/02331880500178521.
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Fitri, Evita, and Dwiza Riana. "ANALISA PERBANDINGAN MODEL PREDICTION DALAM PREDIKSI HARGA SAHAM MENGGUNAKAN METODE LINEAR REGRESSION, RANDOM FOREST REGRESSION DAN MULTILAYER PERCEPTRON." METHOMIKA Jurnal Manajemen Informatika dan Komputerisasi Akuntansi 6, no. 1 (2022): 69–78. http://dx.doi.org/10.46880/jmika.vol6no1.pp69-78.
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Stock is one type of long-term investment that is quite in demand by the public because this investment brings quite a large profit for its investors. However, in relation to this, stock price movements, in general, tend to be non-linear and non-stationary, this is because stock prices can be influenced by several factors whose results can change the pattern of stock price values either up or down, so this can make it difficult to stock prices prediction. In this study, a comparative analysis of prediction models was carried out in predicting stock prices using a technical approach based on past data, while the data used were historical stock prices by taking data samples from three issuers from the Indonesian capital market. There are three methods that were tested in this study, including Linear Regression (LR), Random Forest Regression (RFR), and Multilayer Perceptron (MLP). The test was carried out with two data modeling, namely partitioning which was validated with Cross-Validation, and data modeling with Cross-Validation without partitioning. In this study, the prediction model with LR is able to produce a fairly low error prediction value with the lowest RMSE score of 0.010 and the highest RMSE of 0.012, the lowest MAPE of 1.2%, and the highest of 1.9%, the lowest MAE of 0.006 and the highest. of 0.009, and the highest R2 value was 99.8% and the lowest was 99.6%. It can be concluded that in this study, the Linear Regression prediction model is able to predict historical data on stock prices better than the RFR and MLP models.
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Villordon, Arthur, Christopher Clark, Don Ferrin, and Don LaBonte. "Using Growing Degree Days, Agrometeorological Variables, Linear Regression, and Data Mining Methods to Help Improve Prediction of Sweetpotato Harvest Date in Louisiana." HortTechnology 19, no. 1 (2009): 133–44. http://dx.doi.org/10.21273/hortsci.19.1.133.
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Predictive models of optimum sweetpotato (Ipomoea batatas) harvest in relation to growing degree days (GDD) will benefit producers and researchers by ensuring maximum yields and high quality. A GDD system has not been previously characterized for sweetpotato grown in Louisiana. We used a data set of 116 planting dates and used a combination of minimum cv, linear regression (LR), and several algorithms in a data mining (DM) mode to identify candidate methods of estimating relationships between GDD and harvest dates. These DM algorithms included neural networks, support vector machine, multivariate adaptive regression splines, regression trees, and generalized linear models. We then used candidate GDD methods along with agrometeorological variables to model US#1 yield using LR and DM methodology. A multivariable LR model with the best adjusted r2 was based on GDD calculated using this method: maximum daily temperature (Tmax) – base temperature (B), where if Tmax > ceiling temperature [C (90 °F)], then Tmax = C, and where GDD = 0 if minimum daily temperature <60 °F. The following climate-related variables contributed to the improvement of adjusted r2 of the LR model: mean relative humidity 20 days after transplanting (DAT), maximum air temperature 20 DAT, and maximum soil temperature 10 DAT (log 10 transformed). In the DM mode, this GDD method and the LR model also demonstrated high predictive accuracy as quantified using mean square error. Using this model, we propose to schedule test harvests at GDD = 2600. The harvest date can further be optimized by predicting US#1 yield using GDD in combination with climate-based predictor variables measured within 20 DAT.
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Villordon, Arthur, Christopher Clark, Don Ferrin, and Don LaBonte. "Using Growing Degree Days, Agrometeorological Variables, Linear Regression, and Data Mining Methods to Help Improve Prediction of Sweetpotato Harvest Date in Louisiana." HortTechnology 19, no. 1 (2009): 133–44. http://dx.doi.org/10.21273/horttech.19.1.133.
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Predictive models of optimum sweetpotato (Ipomoea batatas) harvest in relation to growing degree days (GDD) will benefit producers and researchers by ensuring maximum yields and high quality. A GDD system has not been previously characterized for sweetpotato grown in Louisiana. We used a data set of 116 planting dates and used a combination of minimum cv, linear regression (LR), and several algorithms in a data mining (DM) mode to identify candidate methods of estimating relationships between GDD and harvest dates. These DM algorithms included neural networks, support vector machine, multivariate adaptive regression splines, regression trees, and generalized linear models. We then used candidate GDD methods along with agrometeorological variables to model US#1 yield using LR and DM methodology. A multivariable LR model with the best adjusted r2 was based on GDD calculated using this method: maximum daily temperature (Tmax) – base temperature (B), where if Tmax > ceiling temperature [C (90 °F)], then Tmax = C, and where GDD = 0 if minimum daily temperature <60 °F. The following climate-related variables contributed to the improvement of adjusted r2 of the LR model: mean relative humidity 20 days after transplanting (DAT), maximum air temperature 20 DAT, and maximum soil temperature 10 DAT (log 10 transformed). In the DM mode, this GDD method and the LR model also demonstrated high predictive accuracy as quantified using mean square error. Using this model, we propose to schedule test harvests at GDD = 2600. The harvest date can further be optimized by predicting US#1 yield using GDD in combination with climate-based predictor variables measured within 20 DAT.
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Moon, Hyungsik Roger, and Martin Weidner. "DYNAMIC LINEAR PANEL REGRESSION MODELS WITH INTERACTIVE FIXED EFFECTS." Econometric Theory 33, no. 1 (2015): 158–95. http://dx.doi.org/10.1017/s0266466615000328.
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We analyze linear panel regression models with interactive fixed effects and predetermined regressors, for example lagged-dependent variables. The first-order asymptotic theory of the least squares (LS) estimator of the regression coefficients is worked out in the limit where both the cross-sectional dimension and the number of time periods become large. We find two sources of asymptotic bias of the LS estimator: bias due to correlation or heteroscedasticity of the idiosyncratic error term, and bias due to predetermined (as opposed to strictly exogenous) regressors. We provide a bias-corrected LS estimator. We also present bias-corrected versions of the three classical test statistics (Wald, LR, and LM test) and show their asymptotic distribution is a χ2-distribution. Monte Carlo simulations show the bias correction of the LS estimator and of the test statistics also work well for finite sample sizes.
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Liu, Zixuan, and Xiaoyu Yang. "LR-GA Algorithm Based Study on Vegetable Replenishment and Pricing Decision Making." Highlights in Science, Engineering and Technology 82 (January 26, 2024): 258–63. http://dx.doi.org/10.54097/39mw9g48.
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The sales volume and sales price of vegetable products fluctuate greatly due to factors such as origin, variety and freshness period. Therefore, rational replenishment and pricing decisions are particularly important for supermarkets. Therefore, this paper proposes a replenishment and pricing strategy model for vegetable products based on linear regression-genetic algorithm. In this paper, all vegetable commodities are first classified into four categories using K-mean cluster analysis, and the sales performance of the four categories is observed to understand their sales behaviors and provide references for sales strategies. Then, the cost, price and profit margin of each vegetable commodity are calculated using the weighted method, and the linear regression equation between the sales volume of vegetable commodities and the weighted sales price is given. Finally, using the total profit formula considering the loss rate of each item as the objective function and the linear regression equation between the total sales volume of each vegetable item and the weighted sales price as the constraints, the optimization search is carried out by using linear regression and Genetic Algorithm (LR-GA) to find out the sales price and the sales volume under the maximum profit so as to realize the sales strategy.
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Aguilar, Fernando J., Abderrahim Nemmaoui, Manuel A. Aguilar, and Alberto Peñalver. "Building Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression." Applied Sciences 11, no. 21 (2021): 10139. http://dx.doi.org/10.3390/app112110139.
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Most of the allometric models used to estimate tree aboveground biomass rely on tree diameter at breast height (DBH). However, it is difficult to measure DBH from airborne remote sensors, and is common to draw upon traditional least squares linear regression models to relate DBH with dendrometric variables measured from airborne sensors, such as tree height (H) and crown diameter (CD). This study explores the usefulness of ensemble-type supervised machine learning regression algorithms, such as random forest regression (RFR), categorical boosting (CatBoost), gradient boosting (GBoost), or AdaBoost regression (AdaBoost), as an alternative to linear regression (LR) for modelling the allometric relationships DBH = Φ(H) and DBH = Ψ(H, CD). The original dataset was made up of 2272 teak trees (Tectona grandis Linn. F.) belonging to three different plantations located in Ecuador. All teak trees were digitally reconstructed from terrestrial laser scanning point clouds. The results showed that allometric models involving both H and CD to estimate DBH performed better than those based solely on H. Furthermore, boosting machine learning regression algorithms (CatBoost and GBoost) outperformed RFR (bagging) and LR (traditional linear regression) models, both in terms of goodness-of-fit (R2) and stability (variations in training and testing samples).
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Lee, Myung-Won, and Keun-Chang Kwak. "An Incremental Radial Basis Function Network Based on Information Granules and Its Application." Computational Intelligence and Neuroscience 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/3207627.
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This paper is concerned with the design of an Incremental Radial Basis Function Network (IRBFN) by combining Linear Regression (LR) and local RBFN for the prediction of heating load and cooling load in residential buildings. Here the proposed IRBFN is designed by building a collection of information granules through Context-based Fuzzy C-Means (CFCM) clustering algorithm that is guided by the distribution of error of the linear part of the LR model. After adopting a construct of a LR as global model, refine it through local RBFN that captures remaining and more localized nonlinearities of the system to be considered. The experiments are performed on the estimation of energy performance of 768 diverse residential buildings. The experimental results revealed that the proposed IRBFN showed good performance in comparison to LR, the standard RBFN, RBFN with information granules, and Linguistic Model (LM).
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Miranda-Vega, J. E., M. Rivas-López, W. Flores-Fuentes, O. Sergiyenko, L. Lindner, and J. C. Rodríguez-Quiñonez. "Reconocimiento de patrones aplicando LDA y LR a señales optoelectrónicas de sistemas de barrido óptico." Revista Iberoamericana de Automática e Informática industrial 17, no. 4 (2020): 401. http://dx.doi.org/10.4995/riai.2020.12385.
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<p class="icsmabstract">Este artículo da seguimiento a previas experimentaciones actualmente publicadas acerca de la minimización de ruido ópticoeléctrico en los sistemas de barrido óptico OSS (en inglés, Optical Scanning Systems), implementando técnicas computacionales para el reconocimiento de los patrones que se generan de cada fuente de referencia y que son utilizadas para indicar una coordenada que el OSS monitoreará. Técnicas como análisis linear discriminante LDA (en inglés, Linear Discriminant Analysis) y regresión lineal LR (en inglés, Linear Regression) fueron implementadas para discriminar las señales causadas por otras fuentes distintas a las de referencia. Para aumentar la eficiencia de estos modelos fueron implementados codificación predictiva lineal LPC (en inglés, Linear Predictive Coding) y Cuantiles como extractores de características. Los resultados fueron alentadores con tasas de reconocimiento mayores al 91.2 %, alcanzando en algunos casos una exactitud del 100 %.</p>
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Chaitanya, S. K., Siddharth Sriraman, Srinath Srinivasan, and Srinivasan K. "Equivalent source method based Near-field acoustic holography using machine learning." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 6 (2023): 1545–53. http://dx.doi.org/10.3397/in_2022_0213.
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The equivalent source method has been one of the most commonly used methods for sound source localization. It involves equivalent sources spread over the source plane (or region). The pressure fields from these equivalent sources are usually spherical harmonics. But, the spherical harmonic fields are derived for the Sommerfeld boundary condition with no reflection or reverberation. Data-driven methods help perform sound source localization in a reverberant environment when no prior information about the surroundings is available. The methods studied are linear regression (LR) with Adam, linear regression with L-BFGS, multi-layer perceptron (MLP) with one and two hidden layers. The simulations are conducted for two monopoles in rooms with different reverberation times and compared with one norm convex optimization (L1CVX). It is observed that overall, LR with L-BFGS gave the best results. Also, for low reverberation time, LR with L-BFGS was able to localize the sources better than L1CVX.
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Karim, Ferhad Rahim, Serwan Khorsheed Rafiq, Soran Abdrahman Ahmad, Kawa Omar Fqi Mahmood, and Bilal Kamal Mohammed. "Soft Computing Modeling Including Artificial Neural Network, Non-linear, and Linear Regression Models to Predict the Compressive Strength of Sustainable Mortar Modified with Palm Oil Fuel Ash." CONSTRUCTION 4, no. 1 (2024): 52–64. http://dx.doi.org/10.15282/construction.v4i1.10209.
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Producing sustainable concrete and mortar is the idea that have been investigated by many researchers in the world through using waste materials in the mortar or concrete compositions to reduce the thread on the environment. In order to predict the compressive strength of mortar, this article proposes statistical models utilising linear regression (LR), nonlinear regression (NLR), and artificial neural network (ANN) based on experimental data collected from prior research in the field. The pozzolanic material used in mortar is agricultural waste, specifically Palm Oil Fuel Ash (POFA). In order to choose the most efficient model, the proposed models were evaluated using several statistical parameters. When compared to alternative models (Linear regression, nonlinear regression, and ANN), the one developed using ANN proved to be the most efficient in terms of approach, giving lower values for root mean square error (RMSE) and mean absolute error (MAE) which were 5.11, and 4.175 respectively. The suggested ANN model performed well according to the scatter index (SI), and the coefficient of determination value (R2) value was 34% more than the R2 in the LR model and 23% greater in the NLR model.
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Pham, Thi Yen, and Lan Huong Nguyen. "Prognostic Modelling of Biomethane Production from Waste: Application of Extreme Gradient Boosting." International Journal on Computational Engineering 1, no. 1 (2024): 21–26. http://dx.doi.org/10.62527/comien.1.1.4.
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The escalating fossil fuel prices and greenhouse gases need urgent attention for a sustainable solution. The present study explores as modern machine learning approaches can be employed to prognosticate the complex biomethane generation process from organic wastes, like biowaste or food waste. The research investigates the use of organic sludges and how intelligent approaches can be employed to comprehend the complex nonlinear processes involved in biomethane production. Linear regression and Extreme gradient boosting (XGBoost) based prediction-models were developed and assessed employing a diverse set of statistical parameters, including R, R2, Mean Squared Error (MSE), Mean Absolute Error (MAE), and Kling-Gupta Efficiency (KGE). The results show that the XGBoost model beat the classical Linear Regression (LR) model in both the training and testing phases. During training, the XGBoost had an impressive R2 value of 0.99994, indicating a perfect fit to the data. In contrast, LR achieved an R2 value of 0.65464. Similarly, during the test period, XGBoost outperformed LR with R2 values of 0.9553 to 0.9902. Furthermore, XGBoost reduced prediction errors, with significantly lower MSE and MAE values than LR. Taylor’s graph better illustrates the excellent performance of the XGBoost over LR in both training and testing. These data demonstrate the ability of XGBoost to predict biomethane production, as well as its ability to improve the biomethane production process.
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Owari, Yutaka, and Nobuyuki Miyatake. "Prediction of Chronic Lower Back Pain Using the Hierarchical Neural Network: Comparison with Logistic Regression—A Pilot Study." Medicina 55, no. 6 (2019): 259. http://dx.doi.org/10.3390/medicina55060259.
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Background: Many studies have reported on the causes of chronic lower back pain (CLBP). The aim of this study is to identify if the hierarchical neural network (HNN) is superior to a conventional statistical model for CLBP prediction. Linear models, which included multiple regression analysis, were executed for the analysis of the survey data because of the ease of interpretation. The problem with such linear models was that we could not fully consider the influence of interactions caused by a combination of nonlinear relationships and independent variables. Materials and Methods: The subjects in our study were 96 people (30 men aged 72.3 ± 5.6 years and 66 women aged 71.9 ± 5.4 years) who participated at a college health club from 20 July 2016 to 20 March 2017. The HNN and the logistic regression analysis (LR) were used for the prediction of CLBP and the accuracy of each analysis was compared and examined by using our previously reported data. The LR verified the fit using the Hosmer–Lemeshow test. The efficiencies of the two models were compared using receiver performance analysis (ROC), the root mean square error (RMSE), and the deviance (−2 log likelihood). Results: The area under the ROC curve, the RMSE, and the −2 log likelihood for the LR were 0.7163, 0.2581, and 105.065, respectively. The area under the ROC curve, the RMSE, and the log likelihood for the HNN were 0.7650, 0.2483, and 102.787, respectively (the correct answer rates were HNN = 73.3% and LR = 70.8%). Conclusions: On the basis of the ROC curve, the RMSE, and the −2 log likelihood, the performance of the HNN for the prediction probability of CLBP is equal to or higher than the LR. In the future, the HNN may be useful as an index to judge the risk of CLBP for individual patients.
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Kerunwa, A., C. F. Dike, A. H. Ezekiel, and C. E. Okalla. "A NOVEL APPROACH FOR PREDICTING RATE-OF-PENETRATION USING MACHINE LEARNING." Brazilian Journal of Petroleum and Gas 19, no. 1 (2025): 1–13. https://doi.org/10.5419/bjpg2025-0001.
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The key to a successful and economically viable drilling operation lies in optimum drilling aimed at completing drilling activity within the shortest possible period with the least damage to equipment, formation, and environment. However, the increase in complexity requirements for drilling activities has led to several problems such as poor rate of penetration (ROP), which results in an increase in non-productive time (NPT) and in additional associated costs. In this study, Random Forest (RF) from scikit-learn package and Linear Regression (LR) from XlStat software were utilized for machine learning evaluation for ROP prediction. A 150 dataset from a Niger-Delta oilfield was utilized to develop a model which was statistically evaluated and validated, featuring important parameters detected. Study results show the RF regression model recorded R2 of 99.12%, MSE of 9.38%, RMSE of 3.06% and MAE of 1.8%, while LR yielded R2, MSE, MAE and RMSE of 77.5%, 25.42%, 2.56%, and 5.2% respectively. Validation results present RF recorded values closer to the actual ROP in comparison to the ones found using the Linear Regression (LR) model. From the results, feature importance study, plastic viscosity, rotary speed, and standpipe pressure had the highest impact on ROP.
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Lam, Tu-Ngoc, Jiajun Jiang, Min-Cheng Hsu, et al. "Predictions of Lattice Parameters in NiTi High-Entropy Shape-Memory Alloys Using Different Machine Learning Models." Materials 17, no. 19 (2024): 4754. http://dx.doi.org/10.3390/ma17194754.
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This work applied three machine learning (ML) models—linear regression (LR), random forest (RF), and support vector regression (SVR)—to predict the lattice parameters of the monoclinic B19′ phase in two distinct training datasets: previously published ZrO2-based shape-memory ceramics (SMCs) and NiTi-based high-entropy shape-memory alloys (HESMAs). Our findings showed that LR provided the most accurate predictions for ac, am, bm, and cm in NiTi-based HESMAs, while RF excelled in computing βm for both datasets. SVR disclosed the largest deviation between the predicted and actual values of lattice parameters for both training datasets. A combination approach of RF and LR models enhanced the accuracy of predicting lattice parameters of martensitic phases in various shape-memory materials for stable high-temperature applications.
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Alkhammash, Eman H., Abdelmonaim Fakhry Kamel, Saud M. Al-Fattah, and Ahmed M. Elshewey. "Optimized Multivariate Adaptive Regression Splines for Predicting Crude Oil Demand in Saudi Arabia." Discrete Dynamics in Nature and Society 2022 (January 10, 2022): 1–9. http://dx.doi.org/10.1155/2022/8412895.
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This paper presents optimized linear regression with multivariate adaptive regression splines (LR-MARS) for predicting crude oil demand in Saudi Arabia based on social spider optimization (SSO) algorithm. The SSO algorithm is applied to optimize LR-MARS performance by fine-tuning its hyperparameters. The proposed prediction model was trained and tested using historical oil data gathered from different sources. The results suggest that the demand for crude oil in Saudi Arabia will continue to increase during the forecast period (1980–2015). A number of predicting accuracy metrics including Mean Absolute Error (MAE), Median Absolute Error (MedAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and coefficient of determination ( R 2 ) were used to examine and verify the predicting performance for various models. Analysis of variance (ANOVA) was also applied to reveal the predicting result of the crude oil demand in Saudi Arabia and also to compare the actual test data and predict results between different predicting models. The experimental results show that optimized LR-MARS model performs better than other models in predicting the crude oil demand.
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Pohar, Maja, Mateja Blas, and Sandra Turk. "Comparison of logistic regression and linear discriminant analysis." Advances in Methodology and Statistics 1, no. 1 (2004): 143–61. http://dx.doi.org/10.51936/ayrt6204.
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Two of the most widely used statistical methods for analyzing categorical outcome variables are linear discriminant analysis and logistic regression. While both are appropriate for the development of linear classification models, linear discriminant analysis makes more assumptions about the underlying data. Hence, it is assumed that logistic regression is the more flexible and more robust method in case of violations of these assumptions. In this paper we consider the problem of choosing between the two methods, and set some guidelines for proper choice. The comparison between the methods is based on several measures of predictive accuracy. The performance of the methods is studied by simulations. We start with an example where all the assumptions of the linear discriminant analysis are satisfied and observe the impact of changes regarding the sample size, covariance matrix, Mahalanobis distance and direction of distance between group means. Next, we compare the robustness of the methods towards categorisation and non-normality of explanatory variables in a closely controlled way. We show that the results of LDA and LR are close whenever the normality assumptions are not too badly violated, and set some guidelines for recognizing these situations. We discuss the inappropriateness of LDA in all other cases.
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Maekawa, Koichi. "Comparing the Wald, LR and LM tests for heteroscedasticity in a linear regression model." Economics Letters 26, no. 1 (1988): 37–41. http://dx.doi.org/10.1016/0165-1765(88)90048-1.
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Lv, Zunji, Hongxia Yu, Lanxiang Sun, and Peng Zhang. "Composition analysis of ceramic raw materials using laser-induced breakdown spectroscopy and autoencoder neural network." Analytical Methods 14, no. 13 (2022): 1320–28. http://dx.doi.org/10.1039/d1ay02189c.
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We proposed a spectral data feature extraction method that combines the linear regression and sparse and under-complete autoencoder. LR + SUAC can effectively extract the important information in the secondary features.
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Pan, Chao, Cheng Shi, Honglang Mu, Jie Li, and Xinbo Gao. "EEG-Based Emotion Recognition Using Logistic Regression with Gaussian Kernel and Laplacian Prior and Investigation of Critical Frequency Bands." Applied Sciences 10, no. 5 (2020): 1619. http://dx.doi.org/10.3390/app10051619.
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Emotion plays a nuclear part in human attention, decision-making, and communication. Electroencephalogram (EEG)-based emotion recognition has developed a lot due to the application of Brain-Computer Interface (BCI) and its effectiveness compared to body expressions and other physiological signals. Despite significant progress in affective computing, emotion recognition is still an unexplored problem. This paper introduced Logistic Regression (LR) with Gaussian kernel and Laplacian prior for EEG-based emotion recognition. The Gaussian kernel enhances the EEG data separability in the transformed space. The Laplacian prior promotes the sparsity of learned LR regressors to avoid over-specification. The LR regressors are optimized using the logistic regression via variable splitting and augmented Lagrangian (LORSAL) algorithm. For simplicity, the introduced method is noted as LORSAL. Experiments were conducted on the dataset for emotion analysis using EEG, physiological and video signals (DEAP). Various spectral features and features by combining electrodes (power spectral density (PSD), differential entropy (DE), differential asymmetry (DASM), rational asymmetry (RASM), and differential caudality (DCAU)) were extracted from different frequency bands (Delta, Theta, Alpha, Beta, Gamma, and Total) with EEG signals. The Naive Bayes (NB), support vector machine (SVM), linear LR with L1-regularization (LR_L1), linear LR with L2-regularization (LR_L2) were used for comparison in the binary emotion classification for valence and arousal. LORSAL obtained the best classification accuracies (77.17% and 77.03% for valence and arousal, respectively) on the DE features extracted from total frequency bands. This paper also investigates the critical frequency bands in emotion recognition. The experimental results showed the superiority of Gamma and Beta bands in classifying emotions. It was presented that DE was the most informative and DASM and DCAU had lower computational complexity with relatively ideal accuracies. An analysis of LORSAL and the recently deep learning (DL) methods is included in the discussion. Conclusions and future work are presented in the final section.