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Journal articles on the topic 'Classification and Regression Models'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Veselý, A. "Economic classification and regression problems and neural networks." Agricultural Economics (Zemědělská ekonomika) 57, No. 3 (2011): 150–57. http://dx.doi.org/10.17221/50/2010-agricecon.

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Artificial neural networks provide powerful models for solving many economic classifications, as well as regression problems. For example, they were successfully used for the discrimination between healthy economic agents and those prone to bankruptcy, for the inflation-deflation forecasting, for the currency exchange rates prediction, or for the prediction of share prices. At present, the neural models are part of the majority of standard statistical software packages. This paper discusses the basic principles, which the neural network models are based on, and sum up the important principles

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Holling, Heinz. "A Classification of Trivariate Regression Models." Biometrical Journal 28, no. 7 (1986): 783–90. http://dx.doi.org/10.1002/bimj.4710280704.

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Kovvuri, N. Bhargavi, and Suma G.Jaya. "Exploiting Ensemble Learning for Rainfall Prediction using Meta Regressors and Meta Classifiers." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 2379–84. https://doi.org/10.35940/ijeat.C5806.029320.

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Intense rainfall produces flooding even on dry soil. As heavy rainfall is one of the causes for flooding it is necessary to predict the Rainfall to take necessary precautions for people who are living in risk zone areas. Prediction of Rainfall tomorrow is done accurately using Machine Learning regression and classification Techniques. For Rainfall prediction multiple attributes like Windspeed, Precipitation, Cloudcover, Humidity, Temperature and RainfallToday are considered to predict Rainfall Tomorrow. An ensemble approach is used where predictions from Regression models such as Linear Regres

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Ismagilov, Ilyas I., and Gh Alsaied. "CLASSIFICATION OF REGRESSION MODELS AND A METHOD FOR CONSTRUCTING LINEAR FUZZY REGRESSIONS." Innovative Economy: Information, Analytics, Forecasts, no. 2 (2023): 130–38. http://dx.doi.org/10.47576/2949-1894_2023_2_130.

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Olszewski, Pawel, Leszek Gil, Natalia Rak, Tomasz Wolowiec, and Michal Jasienski. "Construction of Regression Models Predicting Lead Times and Classification Models." EUROPEAN RESEARCH STUDIES JOURNAL XXVIΙ, Special Issue 2 (2024): 179–89. http://dx.doi.org/10.35808/ersj/3398.

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Patil, Bhuvaneshwari, Shivanand S. Rumma, and Mallikarjun Hangarge. "REGRESSION AND CLASSIFICATION MODELS FOR HUMAN AGE PREDICTION." Jurnal Ilmiah Ilmu Terapan Universitas Jambi 8, no. 2 (2024): 424–35. http://dx.doi.org/10.22437/jiituj.v8i2.32505.

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This study aims to enhance automated age prediction from facial images, a task with significant potential in security, law enforcement, and Human-Computer Interaction (HCI). While age estimation has seen progress, it remains a challenging problem due to the diverse factors influencing facial aging, such as genetics, environment, lifestyle, and facial expressions. These variations result in individuals of the same chronological age looking markedly different. Most existing age estimation methods rely on computationally intensive pre-trained models, often treated as "black boxes" with predefined

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Räty, Minna, and Annika Kangas. "Localizing general models with classification and regression trees." Scandinavian Journal of Forest Research 23, no. 5 (2008): 419–30. http://dx.doi.org/10.1080/02827580802378826.

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Lu, Kang-Ping, and Shao-Tung Chang. "A fuzzy classification approach to piecewise regression models." Applied Soft Computing 69 (August 2018): 671–88. http://dx.doi.org/10.1016/j.asoc.2018.04.046.

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Irimia-Dieguez, A. I., A. Blanco-Oliver, and M. J. Vazquez-Cueto. "A Comparison of Classification/Regression Trees and Logistic Regression in Failure Models." Procedia Economics and Finance 23 (2015): 9–14. http://dx.doi.org/10.1016/s2212-5671(15)00493-1.

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Irimia-Dieguez, A. I., A. Blanco-Oliver, and M. J. Vazquez-Cueto. "A Comparison of Classification/Regression Trees and Logistic Regression in Failure Models." Procedia Economics and Finance 26 (2015): 23–28. http://dx.doi.org/10.1016/s2212-5671(15)00797-2.

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Singh, Shamy, and J. Dheeba. "Prediction Models in Machine Learning by Classification and Regression." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 2, no. 5 (2015): 07–12. http://dx.doi.org/10.53555/nncse.v2i5.408.

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One of the machine-learning method for constructing prediction models from data is Classification and Regression. By partitioning the data space recursively these models are configuring and in each prediction model are fitting with a simple predictions. Finally, the partitioning can be represented pictorially as a decision tree. Finite number of unordered values are taken for the designing the classification trees and are designed for independent variables. And the prediction error are measured in terms of misclassification cost. Squared difference between the predicted and observed values are

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Singh, Shamy, and J. Dheeba. "Prediction Models in Machine Learning by Classification and Regression." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 3, no. 5 (2016): 07–12. http://dx.doi.org/10.53555/nncse.v3i5.419.

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One of the machine-learning method for constructing prediction models from data is Classification and Regression. By partitioning the data space recursively these models are configuring and in each prediction model are fitting with a simple predictions. Finally, the partitioning can be represented pictorially as a decision tree. Finite number ofunordered values are taken for the designing the classification trees and are designed for independent variables. And the prediction error are measured in terms of misclassification cost. Squared difference between the predicted and observed values are

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13

Jekabsons, Gints, and Marina Uhanova. "Adaptive Regression and Classification Models with Applications in Insurance." Applied Computer Systems 15, no. 1 (2014): 28–31. http://dx.doi.org/10.2478/acss-2014-0004.

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Abstract Nowadays, in the insurance industry the use of predictive modeling by means of regression and classification techniques is becoming increasingly important and popular. The success of an insurance company largely depends on the ability to perform such tasks as credibility estimation, determination of insurance premiums, estimation of probability of claim, detecting insurance fraud, managing insurance risk. This paper discusses regression and classification modeling for such types of prediction problems using the method of Adaptive Basis Function Construction

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Cheung, Rex C. Y., Alexander Aue, and Thomas C. M. Lee. "Consistent Estimation for Partition-Wise Regression and Classification Models." IEEE Transactions on Signal Processing 65, no. 14 (2017): 3662–74. http://dx.doi.org/10.1109/tsp.2017.2698407.

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15

Lebedev, Ilya. "Application of Multilevel Models in Classification and Regression Problems." Informatics and Automation 22, no. 3 (2023): 487–510. http://dx.doi.org/10.15622/ia.22.3.1.

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There is a constant need to create methods for improving the quality indicators of information processing. In most practical cases, the ranges of target variables and predictors are formed under the influence of external and internal factors. Phenomena such as concept drift cause the model to lose its completeness and accuracy over time. The purpose of the work is to improve the processing data samples quality based on multi-level models for classification and regression problems. A two-level data processing architecture is proposed. At the lower level, the analysis of incoming information flo

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Bony, Md Nad Vi Al, Pritom Das, Tamanna Pervin, et al. "COMPARATIVE PERFORMANCE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BUSINESS INTELLIGENCE: A STUDY ON CLASSIFICATION AND REGRESSION MODELS." Frontline Marketing, Management and Economics Journal 04, no. 11 (2024): 72–92. https://doi.org/10.37547/marketing-fmmej-04-11-06.

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This study presents a comparative analysis of five widely used machine learning algorithms—Logistic Regression, Support Vector Machines (SVM), Random Forest, Gradient Boosting, and Neural Networks—in the context of business intelligence (BI). The performance of these models was evaluated on both classification and regression tasks, utilizing a comprehensive set of metrics including accuracy, precision, recall, F1 score, AUC-ROC for classification, and R-squared for regression. Results indicate that ensemble models, particularly Random Forest and Gradient Boosting, outperformed other algorithms

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Ridwana, Iffat, Nabil Nassif, and Wonchang Choi. "Modeling of Building Energy Consumption by Integrating Regression Analysis and Artificial Neural Network with Data Classification." Buildings 10, no. 11 (2020): 198. http://dx.doi.org/10.3390/buildings10110198.

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With the constant expansion of the building sector as a major energy consumer in the modern world, the significance of energy-efficient building systems cannot be more emphasized. Most of the buildings are now equipped with an electric dashboard to record consumption data which presents a significant scope of research by utilizing those data in energy modeling. This paper investigates conventional regression modeling in building energy estimation and proposes three models with data classifications to improve their performance. The proposed models are regression models and an artificial neural

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Li, Guowei, Shanwei Yang, Sai Li, Jie Jian, and Juan Li. "Application and Research based on ANOVA and Logistic Regression Models." Frontiers in Computing and Intelligent Systems 5, no. 1 (2023): 100–102. http://dx.doi.org/10.54097/fcis.v5i1.11957.

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The Silk Road was a passage for cultural exchanges between China and the West in ancient times, of which glass was valuable material evidence of early trade exchanges, and China's early glass also led to different chemical compositions after absorbing some foreign technologies. For example, nowadays, most glass cultural relics are divided into two categories, and the identification, analysis and classification of them and similar problems are also the direction that needs to be studied. In view of such problems, we propose to solve such problems by studying one-way ANOVA and binary classificat

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Rodrigues, Filipe, Mariana Lourenco, Bernardete Ribeiro, and Francisco C. Pereira. "Learning Supervised Topic Models for Classification and Regression from Crowds." IEEE Transactions on Pattern Analysis and Machine Intelligence 39, no. 12 (2017): 2409–22. http://dx.doi.org/10.1109/tpami.2017.2648786.

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Vogt, Michael, and Oliver Linton. "Classification of non-parametric regression functions in longitudinal data models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 79, no. 1 (2016): 5–27. http://dx.doi.org/10.1111/rssb.12155.

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Zhang, Li, Kamlesh Mistry, Chee Peng Lim, and Siew Chin Neoh. "Feature selection using firefly optimization for classification and regression models." Decision Support Systems 106 (February 2018): 64–85. http://dx.doi.org/10.1016/j.dss.2017.12.001.

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22

Reddy, B. Praneeth. "Facial Age Estimation Models for Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35579.

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Automated age estimation from face images is the process of assigning either an exact age or a specific age range to a facial image. In this paper a comparative study of the current techniques suitable for this task is performed, with an emphasis on lightweight models suitable for implementation. We investigate both the suitable modern deep learning architectures for feature extraction and the variants of framing the problem itself as either classification, regression or soft label classification. The models are evaluated on Audience dataset for age group classification and FG-NET dataset for

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Altamimi, Ahmad Mousa, Mohammad Azzeh, and Mahmoud Albashayreh. "Predicting students' learning styles using regression techniques." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 1177–85. https://doi.org/10.11591/ijeecs.v25.i2.pp1177-1185.

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Traditional learning systems have responded quickly to the COVID pandemic and moved to online or distance learning. Online learning requires a personalization method because the interaction between learners and instructors is minimal, and learners have a specific learning method that works best for them. One of the personalization methods is detecting the learners' learning style. To detect learning styles, several works have been proposed using classification techniques. However, the current detection models become ineffective when learners have no dominant style or a mix of learning styl

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24

Qiao, Mengke, and Ke-Wei Huang. "Correcting Misclassification Bias in Regression Models with Variables Generated via Data Mining." Information Systems Research 32, no. 2 (2021): 462–80. http://dx.doi.org/10.1287/isre.2020.0977.

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There is a surge of interest in social science studies in applying data mining methods to construct variables for regression analysis. For example, text classification was applied to classify whether the review is subjective or objective. The derived review subjectivity was used as an independent variable in the regression to examine its impact on review helpfulness. In the classification phase of these studies, researchers need to subjectively choose a classification performance metric for optimization. No matter which performance metric is chosen, the constructed variable still includes clas

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Tabatabai, Mohammad, Derek Wilus, Chau-Kuang Chen, Karan P. Singh, and Tim L. Wallace. "Taba Binary, Multinomial, and Ordinal Regression Models: New Machine Learning Methods for Classification." Bioengineering 12, no. 1 (2024): 2. https://doi.org/10.3390/bioengineering12010002.

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The classification methods of machine learning have been widely used in almost every discipline. A new classification method, called Taba regression, was introduced for analyzing binary, multinomial, and ordinal outcomes. To evaluate the performance of Taba regression, liver cirrhosis data obtained from a Mayo Clinic study were analyzed. The results were then compared with an artificial neural network (ANN), random forest (RF), logistic regression (LR), and probit analysis (PA). The results using cirrhosis data revealed that the Taba regression model could be a competitor to other classificati

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Carrizosa, Emilio, Cristina Molero-Río, and Dolores Romero Morales. "Mathematical optimization in classification and regression trees." TOP 29, no. 1 (2021): 5–33. http://dx.doi.org/10.1007/s11750-021-00594-1.

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AbstractClassification and regression trees, as well as their variants, are off-the-shelf methods in Machine Learning. In this paper, we review recent contributions within the Continuous Optimization and the Mixed-Integer Linear Optimization paradigms to develop novel formulations in this research area. We compare those in terms of the nature of the decision variables and the constraints required, as well as the optimization algorithms proposed. We illustrate how these powerful formulations enhance the flexibility of tree models, being better suited to incorporate desirable properties such as

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27

Li, Yanbo. "Enhanced Logistic Regression Using Stacking Algorithm for Imbalanced and High-Dimensional Data." Highlights in Science, Engineering and Technology 136 (March 31, 2025): 1–11. https://doi.org/10.54097/xmphgz15.

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In binary classification tasks, logistic regression models often perform poorly and can even fail when dealing with issues such as class imbalance and the curse of dimensionality. To address these problems, this paper proposes an improved logistic regression model based on the stacking approach. First, the method constructs multiple logistic regression sub-models by employing a dual randomization strategy on both samples and features. The specific strategy involves retaining a small number of minority class samples and drawing a corresponding number of samples from the majority class in propor

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Li, Xinyi. "Improved Logistic Regression Model Based on Resampling Techniques." Highlights in Science, Engineering and Technology 136 (March 31, 2025): 28–36. https://doi.org/10.54097/dnbeak17.

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When facing imbalanced samples and high-dimensional features, the performance of traditional logistic regression models may significantly deteriorate, even becoming completely ineffective. Therefore, this paper proposes an improved logistic regression method combined with resampling techniques. Firstly, the proposed method uses the "resampling and encoding" strategy to effectively capture the predictive information that has a significant impact on the classification result while addressing the problems of imbalanced samples and dimension curse. Secondly, the proposed method uses weighted combi

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Kravets, Petro, Volodymyr Pasichnyk, and Mykola Prodaniuk. "Mathematical Model of Logistic Regression for Binary Classification. Part 1. Regression Models of Data Generalization." Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 15 (July 15, 2024): 290–321. http://dx.doi.org/10.23939/sisn2024.15.290.

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In this article, the mathematical justification of logistic regression as an effective and simple to implement method of machine learning is performed. A review of literary sources was conducted in the direction of statistical processing, analysis and classification of data using the logistic regression method, which confirmed the popularity of this method in various subject areas. The logistic regression method was compared with the linear and probit regression methods regarding the possibility of predicting the probabilities of events. In this context, the disadvantages of linear regression

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Wagner, Rosa de Oliveira, Wust Eduardo, and Loss Jeferson Fagundes. "Analysis of Torque and Stiffness Parameters in Different Cleat Models: Identification of Key Characteristics Using Regression Models." Journal of Sports Medicine and Therapy 10, no. 1 (2025): 001–15. https://doi.org/10.29328/journal.jsmt.1001090.

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This study investigated the relationship between the physical characteristics of soccer cleats and their rotational resistance, aiming to identify the factors that influence maximum torque, stiffness, and the work required for rotation. Fifty-eight cleat models were analyzed, covering different classifications (FirmGround, HardGround, SoftGround, Turf). Tests were conducted in a controlled laboratory setting, utilizing X-ray fluorescence spectroscopy for material analysis, and a rigidimeter for longitudinal stiffness, and a 3D scanner for stud characterization. Rotational resistance was measur

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Rao, C. R., Y. Wu, and Q. Shao. "An M-Estimation-Based Procedure for Determining the Number of Regression Models in Regression Clustering." Journal of Applied Mathematics and Decision Sciences 2007 (October 31, 2007): 1–15. http://dx.doi.org/10.1155/2007/37475.

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In this paper, a procedure based on M-estimation to determine the number of regression models for the problem of regression clustering is proposed. We have shown that the true classification is attained when n increases to infinity under certain mild conditions, for instance, without assuming normality of the distribution of the random errors in each regression model.

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Górecki, Tomasz, Mirosław Krzyśko, and Waldemar Wołyński. "CLASSIFICATION PROBLEMS BASED ON REGRESSION MODELS FOR MULTI-DIMENSIONAL FUNCTIONAL DATA." Statistics in Transition. New Series 16, no. 1 (2015): 97–110. http://dx.doi.org/10.21307/stattrans-2015-006.

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Lim, Noha, Hongshik Ahn, Hojin Moon, and James J. Chen. "Classification of High-Dimensional Data with Ensemble of Logistic Regression Models." Journal of Biopharmaceutical Statistics 20, no. 1 (2009): 160–71. http://dx.doi.org/10.1080/10543400903280639.

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34

Chen, Jing, Long Cheng, Xi Yang, Jun Liang, Bing Quan, and Shoushan Li. "Joint Learning with both Classification and Regression Models for Age Prediction." Journal of Physics: Conference Series 1168 (February 2019): 032016. http://dx.doi.org/10.1088/1742-6596/1168/3/032016.

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Rice, John D., and Jeremy M. G. Taylor. "Locally Weighted Score Estimation for Quantile Classification in Binary Regression Models." Statistics in Biosciences 8, no. 2 (2016): 333–50. http://dx.doi.org/10.1007/s12561-016-9147-y.

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36

Dreiseitl, Stephan, and Lucila Ohno-Machado. "Logistic regression and artificial neural network classification models: a methodology review." Journal of Biomedical Informatics 35, no. 5-6 (2002): 352–59. http://dx.doi.org/10.1016/s1532-0464(03)00034-0.

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Kotsiantis, Sotiris B., and Dimitris N. Kanellopoulos. "Bagging different instead of similar models for regression and classification problems." International Journal of Computer Applications in Technology 37, no. 1 (2010): 20. http://dx.doi.org/10.1504/ijcat.2010.030472.

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Górecki, Tomasz, Mirosław Krzyśko, and Waldemar Wołyński. "Classification problems based on regression models for multi-dimensional functional data." Statistics in Transition new series 16, no. 1 (2015): 97–110. http://dx.doi.org/10.59170/stattrans-2015-006.

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Data in the form of a continuous vector function on a given interval are referred to as multivariate functional data. These data are treated as realizations of multivariate random processes. We use multivariate functional regression techniques for the classification of multivariate functional data. The approaches discussed are illustrated with an application to two real data sets.

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Li, Xianzong. "Smartphone Price Prediction Using Decision Tree and Support Vector Regression (SVR)." Applied and Computational Engineering 115, no. 1 (2024): 43–49. https://doi.org/10.54254/2755-2721/2025.18475.

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This paper explores the use of two machine learning models, Decision Tree and Support Vector Regression (SVR), for smartphone price prediction. Decision Trees provide a straightforward and efficient classification method, while SVR specializes in managing complex relationships for regression tasks. The study compares the performance of these models in predicting smartphone prices, analyzing key factors such as processor speed, memory, and battery capacity. Additionally, a combined model approach that combines Decision Tree for classification and SVR for regression is proposed to improve predic

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Thorson, Jacob, Ashley Collier-Oxandale, and Michael Hannigan. "Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources." Sensors 19, no. 17 (2019): 3723. http://dx.doi.org/10.3390/s19173723.

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An array of low-cost sensors was assembled and tested in a chamber environment wherein several pollutant mixtures were generated. The four classes of sources that were simulated were mobile emissions, biomass burning, natural gas emissions, and gasoline vapors. A two-step regression and classification method was developed and applied to the sensor data from this array. We first applied regression models to estimate the concentrations of several compounds and then classification models trained to use those estimates to identify the presence of each of those sources. The regression models that w

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Jittawiriyanukoon, Chanintorn. "Proposed algorithm for image classification using regression-based pre-processing and recognition models." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 1021–27. https://doi.org/10.11591/ijece.v9i2.pp1021-1027.

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Image classification algorithms can categorize pixels regarding image attributes with the pre-processing of learner’s trained samples. The precision and classification accuracy are complex to compute due to the variable size of pixels (different image width and height) and numerous characteristics of image per se. This research proposes an image classification algorithm based on regression-based pre-processing and the recognition models. The proposed algorithm focuses on an optimization of pre-processing results such as accuracy and precision. To evaluate and validate, the recognition mo

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Jittawiriyanukoon, Chanintorn. "Proposed algorithm for image classification using regression-based pre-processing and recognition models." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 1021. http://dx.doi.org/10.11591/ijece.v9i2.pp1021-1027.

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<span>Image classification algorithms can categorise pixels regarding to image attributes with the pre-processing of learner’s trained samples. The precision and classification accuracy are complex to compute due to the variable size of pixels (different image width and height) and numerous characteristics of image per se. This research proposes an image classification algorithm based on regression-based pre-processing and the recognition models. The proposed algorithm focuses on an optimization of pre-processing results such as accuracy and precision. To evaluate and validate, recogniti

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43

Altamimi, Ahmad Mousa, Mohammad Azzeh, and Mahmoud Albashayreh. "Predicting students' learning styles using regression techniques." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 1177. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp1177-1185.

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Traditional learning systems have responded quickly to the COVID pandemic and moved to online or distance learning. Online learning requires a personalization method because the interaction between learners and instructors is minimal, and learners have a specific learning method that works best for them. One of the personalization methods is detecting the learners' learning style. To detect learning styles, several works have been proposed using classification techniques. However, the current detection models become ineffective when learners have no dominant style or a mix of learning styles.

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Siirtola, Pekka, and Juha Röning. "Comparison of Regression and Classification Models for User-Independent and Personal Stress Detection." Sensors 20, no. 16 (2020): 4402. http://dx.doi.org/10.3390/s20164402.

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In this article, regression and classification models are compared for stress detection. Both personal and user-independent models are experimented. The article is based on publicly open dataset called AffectiveROAD, which contains data gathered using Empatica E4 sensor and unlike most of the other stress detection datasets, it contains continuous target variables. The used classification model is Random Forest and the regression model is Bagged tree based ensemble. Based on experiments, regression models outperform classification models, when classifying observations as stressed or not-stress

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A, G. Priya Varshini, and Anitha Kumari K. "Predictive analytics approaches for software effort estimation: A review." Indian Journal of Science and Technology 13, no. 21 (2020): 2094–103. https://doi.org/10.17485/IJST/v13i21.573.

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Abstract <strong>Background/Objective:</strong> In Software Effort Estimation (SEE), predicting the amount of time taken in human hours or months for software development is considered as a cumbersome process. SEE consists of both Software Development Effort Estimation (SDEE) and Software Maintenance Effort Estimation (SMEE). Over estimation or under estimation of software effort results in project cancellation or project failure. The objective of this study is to identify the best performing model for software Effort Estimation through experimental comparison with various Machine learnin

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YANG, GUAN, GUO-CAN FENG, ZHI-HONG LUO, and ZHI-YONG LIU. "TEXTURE ANALYSIS USING GAUSSIAN GRAPHICAL MODELS." International Journal of Wavelets, Multiresolution and Information Processing 10, no. 02 (2012): 1250015. http://dx.doi.org/10.1142/s0219691312500154.

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Texture classification is a challenging and important problem in image analysis. graphical models (GM) are promising tools for texture analysis. In this paper, we address the problem of learning the structure of Gaussian graphical models (GGM) for texture models. GGM can be considered as regression problems due to the connection between the local Markov properties and conditional regression of a Gaussian random variable. We utilize L1-penalty regularization technique for appropriate neighborhood selection and parameter estimation simultaneously. The proposed algorithms are applied in texture s

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Yan, Jihong, and Jay Lee. "Degradation Assessment and Fault Modes Classification Using Logistic Regression." Journal of Manufacturing Science and Engineering 127, no. 4 (2004): 912–14. http://dx.doi.org/10.1115/1.1962019.

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Real-time health monitoring of industrial components and systems that can detect, classify and predict impending faults is critical to reducing operating and maintenance cost. This paper presents a logistic regression based prognostic method for on-line performance degradation assessment and failure modes classification. System condition is evaluated by processing the information gathered from controllers or sensors mounted at different points in the system, and maintenance is performed only when the failure∕malfunction prognosis indicates instead of periodic maintenance inspections. The wavel

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Limbu, Sarita, and Sivanesan Dakshanamurthy. "Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method." Sensors 22, no. 21 (2022): 8185. http://dx.doi.org/10.3390/s22218185.

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Determining environmental chemical carcinogenicity is urgently needed as humans are increasingly exposed to these chemicals. In this study, we developed a hybrid neural network (HNN) method called HNN-Cancer to predict potential carcinogens of real-life chemicals. The HNN-Cancer included a new SMILES feature representation method by modifying our previous 3D array representation of 1D SMILES simulated by the convolutional neural network (CNN). We developed binary classification, multiclass classification, and regression models based on diverse non-congeneric chemicals. Along with the HNN-Cance

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Wang, Yunshang. "Logistic Regression Model to Personality Type Prediction Based on the Myers–Briggs Type Indicator." Transactions on Computer Science and Intelligent Systems Research 7 (November 25, 2024): 206–15. https://doi.org/10.62051/4d9gv137.

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The Myers-Briggs Type Indicator (MBTI) is a widely-used tool in psychology for determining personality types, playing a crucial role in fields like team building, communication, and personalized marketing. Despite its popularity, accurately classifying MBTI types using machine learning remains a significant challenge. This study focuses on addressing this challenge by exploring the effectiveness of logistic regression in MBTI classification tasks. Two approaches are used: four-times binary classification and multi-class classification. The findings show that while logistic regression performs

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Narasimhan, Harikrishna, Andrew Cotter, Maya Gupta, and Serena Wang. "Pairwise Fairness for Ranking and Regression." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5248–55. http://dx.doi.org/10.1609/aaai.v34i04.5970.

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We present pairwise fairness metrics for ranking models and regression models that form analogues of statistical fairness notions such as equal opportunity, equal accuracy, and statistical parity. Our pairwise formulation supports both discrete protected groups, and continuous protected attributes. We show that the resulting training problems can be efficiently and effectively solved using existing constrained optimization and robust optimization techniques developed for fair classification. Experiments illustrate the broad applicability and trade-offs of these methods.

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