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Journal articles on the topic 'Lasso feature selection'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Muthukrishnan, R., and C. K. James. "The Effect of Multicollinearity on Feature Selection." Indian Journal Of Science And Technology 17, no. 35 (2024): 3664–68. http://dx.doi.org/10.17485/ijst/v17i35.1876.

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Objectives: To provide a new LASSO-based feature selection technique that aids in selecting important variables for predicting the response variables in case of multicollinearity. Methods: LASSO is a type of regression method employed to select important covariates for predicting a dependent variable. The traditional LASSO method uses the conventional Ordinary Least Square (OLS) method for this purpose. The Use of the OLS based LASSO approach gives unreliable results if the data deviates from normality. Thus, this study recommends using, a Redescending M-estimator-based LASSO approach. The eff

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R, Muthukrishnan, and K. James C. "The Effect of Multicollinearity on Feature Selection." Indian Journal of Science and Technology 17, no. 35 (2024): 3664–68. https://doi.org/10.17485/IJST/v17i35.1876.

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Abstract <strong>Objectives:</strong> To provide a new LASSO-based feature selection technique that aids in selecting important variables for predicting the response variables in case of multicollinearity. <strong>Methods:</strong> LASSO is a type of regression method employed to select important covariates for predicting a dependent variable. The traditional LASSO method uses the conventional Ordinary Least Square (OLS) method for this purpose. The Use of the OLS based LASSO approach gives unreliable results if the data deviates from normality. Thus, this study recommends using

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Jain, Rahi, and Wei Xu. "HDSI: High dimensional selection with interactions algorithm on feature selection and testing." PLOS ONE 16, no. 2 (2021): e0246159. http://dx.doi.org/10.1371/journal.pone.0246159.

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Feature selection on high dimensional data along with the interaction effects is a critical challenge for classical statistical learning techniques. Existing feature selection algorithms such as random LASSO leverages LASSO capability to handle high dimensional data. However, the technique has two main limitations, namely the inability to consider interaction terms and the lack of a statistical test for determining the significance of selected features. This study proposes a High Dimensional Selection with Interactions (HDSI) algorithm, a new feature selection method, which can handle high-dim

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Yamada, Makoto, Wittawat Jitkrittum, Leonid Sigal, Eric P. Xing, and Masashi Sugiyama. "High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso." Neural Computation 26, no. 1 (2014): 185–207. http://dx.doi.org/10.1162/neco_a_00537.

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The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on linear dependency between input features and output values. In this letter, we consider a feature-wise kernelized Lasso for capturing nonlinear input-output dependency. We first show that with particular choices of kernel functions, nonredundant features with strong statistical dependence on output values can be found in terms of kernel-based indep

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K, Emily Esther Rani, and Baulkani S. "Multi Variate Feature Extraction and Feature Selection using LGKFS Algorithm for Detecting Alzheimer's Disease." Indian Journal of Science and Technology 16, no. 22 (2023): 1665–75. https://doi.org/10.17485/IJST/v16i22.707.

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Abstract <strong>Objectives:</strong> This study focuses on machine learning techniques to classify various stages of Alzheimer’s Disease(AD). <strong>Methods:</strong> Absolutely, 1,997 PD weighted Resting State Functional MRI (rsFMRI) images were acquired from ADNI-3 dataset for the classification of AD. First, input rsFMRI images from the dataset were preprocessed and segmented. After segmentation, we have extracted multi variate features. Then, we have proposed Lasso with Graph Kernel Feature Selection (LGKFS) algorithm for selecting the best features. Finally, Radom F

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Huang, Qiang, Tingyu Xia, Huiyan Sun, Makoto Yamada, and Yi Chang. "Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4182–89. http://dx.doi.org/10.1609/aaai.v34i04.5839.

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With the rapid development of social media services in recent years, relational data are explosively growing. The signed network, which consists of a mixture of positive and negative links, is an effective way to represent the friendly and hostile relations among nodes, which can represent users or items. Because the features associated with a node of a signed network are usually incomplete, noisy, unlabeled, and high-dimensional, feature selection is an important procedure to eliminate irrelevant features. However, existing network-based feature selection methods are linear methods, which mea

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Mai, Jifang, Shaohua Zhang, Haiqing Zhao, and Lijun Pan. "Factor Investment or Feature Selection Analysis?" Mathematics 13, no. 1 (2024): 9. https://doi.org/10.3390/math13010009.

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This study has made significant findings in A-share market data processing and portfolio management. Firstly, by adopting the Lasso method and CPCA framework, we effectively addressed the problem of multicollinearity among feature indicators, with the Lasso method demonstrating superior performance in handling this issue, thus providing a new method for financial data processing. Secondly, Deep Feedforward Neural Networks (DFN) exhibited exceptional performance in portfolio management, significantly outperforming other evaluated machine learning methods, and achieving high levels of out-of-sam

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Patil, Abhijeet R., and Sangjin Kim. "Combination of Ensembles of Regularized Regression Models with Resampling-Based Lasso Feature Selection in High Dimensional Data." Mathematics 8, no. 1 (2020): 110. http://dx.doi.org/10.3390/math8010110.

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In high-dimensional data, the performances of various classifiers are largely dependent on the selection of important features. Most of the individual classifiers with the existing feature selection (FS) methods do not perform well for highly correlated data. Obtaining important features using the FS method and selecting the best performing classifier is a challenging task in high throughput data. In this article, we propose a combination of resampling-based least absolute shrinkage and selection operator (LASSO) feature selection (RLFS) and ensembles of regularized regression (ERRM) capable o

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Xie, Zongxia, and Yong Xu. "Sparse group LASSO based uncertain feature selection." International Journal of Machine Learning and Cybernetics 5, no. 2 (2013): 201–10. http://dx.doi.org/10.1007/s13042-013-0156-6.

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Ming, Di, Chris Ding, and Feiping Nie. "A Probabilistic Derivation of LASSO and L12-Norm Feature Selections." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4586–93. http://dx.doi.org/10.1609/aaai.v33i01.33014586.

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LASSO and ℓ2,1-norm based feature selection had achieved success in many application areas. In this paper, we first derive LASSO and ℓ1,2-norm feature selection from a probabilistic framework, which provides an independent point of view from the usual sparse coding point of view. From here, we further propose a feature selection approach based on the probability-derived ℓ1,2-norm. We point out some inflexibility in the standard feature selection that the feature selected for all different classes are enforced to be exactly the same using the widely used ℓ2,1-norm, which enforces the joint spar

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Jo, Jongkwon, Seungha Jung, Joongyang Park, Youngsoon Kim, and Mingon Kang. "Hi-LASSO: High-performance python and apache spark packages for feature selection with high-dimensional data." PLOS ONE 17, no. 12 (2022): e0278570. http://dx.doi.org/10.1371/journal.pone.0278570.

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High-dimensional LASSO (Hi-LASSO) is a powerful feature selection tool for high-dimensional data. Our previous study showed that Hi-LASSO outperformed the other state-of-the-art LASSO methods. However, the substantial cost of bootstrapping and the lack of experiments for a parametric statistical test for feature selection have impeded to apply Hi-LASSO for practical applications. In this paper, the Python package and its Spark library are efficiently designed in a parallel manner for practice with real-world problems, as well as providing the capability of the parametric statistical tests for

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Alanezi, Saleh T., Marcin Jan Kraśny, Christoph Kleefeld, and Niall Colgan. "Differential Diagnosis of Prostate Cancer Grade to Augment Clinical Diagnosis Based on Classifier Models with Tuned Hyperparameters." Cancers 16, no. 11 (2024): 2163. http://dx.doi.org/10.3390/cancers16112163.

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We developed a novel machine-learning algorithm to augment the clinical diagnosis of prostate cancer utilizing first and second-order texture analysis metrics in a novel application of machine-learning radiomics analysis. We successfully discriminated between significant prostate cancers versus non-tumor regions and provided accurate prediction between Gleason score cohorts with statistical sensitivity of 0.82, 0.81 and 0.91 in three separate pathology classifications. Tumor heterogeneity and prediction of the Gleason score were quantified using two feature selection approaches and two separat

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Cahigas, Maela Madel L., Ardvin Kester S. Ong, and Yogi Tri Prasetyo. "Super Typhoon Rai’s Impacts on Siargao Tourism: Deciphering Tourists’ Revisit Intentions through Machine-Learning Algorithms." Sustainability 15, no. 11 (2023): 8463. http://dx.doi.org/10.3390/su15118463.

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Super Typhoon Rai damaged Siargao’s tourism industry. Despite the reconstruction projects, there was still evidence of limited resources, destructed infrastructures, and destroyed natural resources. Therefore, this study aimed to examine the significant factors influencing tourists’ intentions to revisit Siargao after Super Typhoon Rai using feature selection, logistic regression (LR), and an artificial neural network (ANN). It employed three feature-selection techniques, namely, the filter method’s permutation importance (PI), the wrapper method’s Recursive Feature Elimination (RFE), and the

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Li, Shanshan, Jian Yu, Huimin Kang, and Jianfeng Liu. "Genomic Selection in Chinese Holsteins Using Regularized Regression Models for Feature Selection of Whole Genome Sequencing Data." Animals 12, no. 18 (2022): 2419. http://dx.doi.org/10.3390/ani12182419.

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Genomic selection (GS) is an efficient method to improve genetically economic traits. Feature selection is an important method for GS based on whole-genome sequencing (WGS) data. We investigated the prediction performance of GS of milk production traits using imputed WGS data on 7957 Chinese Holsteins. We used two regularized regression models, least absolute shrinkage and selection operator (LASSO) and elastic net (EN) for feature selection. For comparison, we performed genome-wide association studies based on a linear mixed model (LMM), and the N single nucleotide polymorphisms (SNPs) with t

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Cui, Lixin, Lu Bai, Yue Wang, Philip S. Yu, and Edwin R. Hancock. "Fused lasso for feature selection using structural information." Pattern Recognition 119 (November 2021): 108058. http://dx.doi.org/10.1016/j.patcog.2021.108058.

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Zhang, Zhihong, Yiyang Tian, Lu Bai, Jianbing Xiahou, and Edwin Hancock. "High-order covariate interacted Lasso for feature selection." Pattern Recognition Letters 87 (February 2017): 139–46. http://dx.doi.org/10.1016/j.patrec.2016.08.005.

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Coelho, Frederico, Marcelo Costa, Michel Verleysen, and Antônio P. Braga. "LASSO multi-objective learning algorithm for feature selection." Soft Computing 24, no. 17 (2020): 13209–17. http://dx.doi.org/10.1007/s00500-020-04734-w.

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Gramegna, Alex, and Paolo Giudici. "Shapley Feature Selection." FinTech 1, no. 1 (2022): 72–80. http://dx.doi.org/10.3390/fintech1010006.

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Feature selection is a popular topic. The main approaches to deal with it fall into the three main categories of filters, wrappers and embedded methods. Advancement in algorithms, though proving fruitful, may be not enough. We propose to integrate an explainable AI approach, based on Shapley values, to provide more accurate information for feature selection. We test our proposal in a real setting, which concerns the prediction of the probability of default of Small and Medium Enterprises. Our results show that the integrated approach may indeed prove fruitful to some feature selection methods,

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He, Huan, Xinyun Guo, Jialin Yu, Chen Ai, and Shaoping Shi. "Overcoming the inadaptability of sparse group lasso for data with various group structures by stacking." Bioinformatics 38, no. 6 (2021): 1542–49. http://dx.doi.org/10.1093/bioinformatics/btab848.

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Abstract Motivation Efficiently identifying genes based on gene expression level have been studied to help to classify different cancer types and improve the prediction performance. Logistic regression model based on regularization technique is often one of the effective approaches for simultaneously realizing prediction and feature (gene) selection in genomic data of high dimensionality. However, standard methods ignore biological group structure and generally result in poorer predictive models. Results In this article, we develop a classifier named Stacked SGL that satisfies the criteria of

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Wang, Jin-Jia, Fang Xue, and Hui Li. "Simultaneous Channel and Feature Selection of Fused EEG Features Based on Sparse Group Lasso." BioMed Research International 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/703768.

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Feature extraction and classification of EEG signals are core parts of brain computer interfaces (BCIs). Due to the high dimension of the EEG feature vector, an effective feature selection algorithm has become an integral part of research studies. In this paper, we present a new method based on a wrapped Sparse Group Lasso for channel and feature selection of fused EEG signals. The high-dimensional fused features are firstly obtained, which include the power spectrum, time-domain statistics, AR model, and the wavelet coefficient features extracted from the preprocessed EEG signals. The wrapped

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Kimmatkar, N. V., and B. Vijaya Babu. "Human Emotion Detection with Electroencephalography Signals and Accuracy Analysis Using Feature Fusion Techniques and a Multimodal Approach for Multiclass Classification." Engineering, Technology & Applied Science Research 12, no. 4 (2022): 9012–17. http://dx.doi.org/10.48084/etasr.5073.

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Biological brain signals may be used to identify emotions in a variety of ways, with accuracy depended on the methods used for signal processing, feature extraction, feature selection, and classification. The major goal of the current work was to use an adaptive channel selection and classification strategy to improve the effectiveness of emotion detection utilizing brain signals. Using different features picked by feature fusion approaches, the accuracy of existing classification models' emotion detection is assessed. Statistical modeling is used to determine time-domain and frequency-domain

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Felcia, Bel, and Sabeen Sabeen. "Hybrid optimal feature selection approach for internet of things based medical data analysis for prognosis." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 2011–18. https://doi.org/10.11591/ijai.v13.i2.pp2011-2018.

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Healthcare is very important application domain in internet of things (IoT). The aim is to provide a novel combined feature selection (FS) methods like univariate (UV) with tree-based methods (TB), recursive feature elimination (RFE) with least absolute shrinkage selection operator (LASSO), mutual information (MI) with genetic algorithm (GA) and embedded methods (EM) with univariate has been applied to internet of medical things (IoMT)based heart disease dataset. The well-suited machine learning algorithms for IoT medical data are logistic regression (LR) and support vector machine (SVM). Each

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Hajira Be, A. B. "Feature Selection and Classification with the Annealing Optimization Deep Learning for the Multi-Modal Image Processing." Journal of Computer Allied Intelligence 2, no. 3 (2024): 55–66. http://dx.doi.org/10.69996/jcai.2024015.

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This paper investigates and compares various feature selection algorithms within the context of image processing across multiple datasets. The study evaluates Seahorse Annealing Optimization for Feature Selection (SAO-FS), Genetic Algorithms (GA), CNN + Feature Fusion Network, and Lasso Regression on distinct image datasets—medical images, satellite images, MRI scans, and microscopy images. Performance metrics including accuracy, precision, recall, and computational time are analyzed to assess the efficacy of each algorithm in optimizing feature subsets for classification tasks. SAO-FS demonst

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Gillies, Christopher E., Xiaoli Gao, Nilesh V. Patel, Mohammad-Reza Siadat, and George D. Wilson. "Improved Feature Selection by Incorporating Gene Similarity into the LASSO." International Journal of Knowledge Discovery in Bioinformatics 3, no. 1 (2012): 1–22. http://dx.doi.org/10.4018/jkdb.2012010101.

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Personalized medicine is customizing treatments to a patient’s genetic profile and has the potential to revolutionize medical practice. An important process used in personalized medicine is gene expression profiling. Analyzing gene expression profiles is difficult, because there are usually few patients and thousands of genes, leading to the curse of dimensionality. To combat this problem, researchers suggest using prior knowledge to enhance feature selection for supervised learning algorithms. The authors propose an enhancement to the LASSO, a shrinkage and selection technique that induces pa

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Frank, Laurence E., and Willem J. Heiser. "Feature selection in feature network models: Finding predictive subsets of features with the Positive Lasso." British Journal of Mathematical and Statistical Psychology 61, no. 1 (2008): 1–27. http://dx.doi.org/10.1348/000711006x119365.

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Adamu, Buba, Usman Umar, Musa Yakubu, and Muhammed Hamza Murtala. "On Some New Hybridized Regression Estimation and Feature Selection Techniques." On Some New Hybridized Regression Estimation and Feature Selection Techniques 8, no. 9 (2023): 15. https://doi.org/10.5281/zenodo.10029183.

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Conventional regularization techniques like LASSO, SCAD and MCP have been shown to perform poorly in the presence of extremely large or ultra-high dimensional covariates. This has created the need for and led to the development and reliance on filtering technique like screening. Screening techniques (such as SIS, DC-SIS, and DC – RoSIS) have been shown to reduce the computational complexity in selecting important covariates from ultrahigh dimensional candidates. To this end, there have been various attempts to hybridize the conventional regularization techniques. In this paper, we combine some

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Tasci, Erdal, Ying Zhuge, Harpreet Kaur, Kevin Camphausen, and Andra Valentina Krauze. "Hierarchical Voting-Based Feature Selection and Ensemble Learning Model Scheme for Glioma Grading with Clinical and Molecular Characteristics." International Journal of Molecular Sciences 23, no. 22 (2022): 14155. http://dx.doi.org/10.3390/ijms232214155.

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Determining the aggressiveness of gliomas, termed grading, is a critical step toward treatment optimization to increase the survival rate and decrease treatment toxicity for patients. Streamlined grading using molecular information has the potential to facilitate decision making in the clinic and aid in treatment planning. In recent years, molecular markers have increasingly gained importance in the classification of tumors. In this study, we propose a novel hierarchical voting-based methodology for improving the performance results of the feature selection stage and machine learning models fo

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Lin, Bingqing, Zhen Pang, and Qihua Wang. "Cluster feature selection in high-dimensional linear models." Random Matrices: Theory and Applications 07, no. 01 (2018): 1750015. http://dx.doi.org/10.1142/s2010326317500150.

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This paper concerns with variable screening when highly correlated variables exist in high-dimensional linear models. We propose a novel cluster feature selection (CFS) procedure based on the elastic net and linear correlation variable screening to enjoy the benefits of the two methods. When calculating the correlation between the predictor and the response, we consider highly correlated groups of predictors instead of the individual ones. This is in contrast to the usual linear correlation variable screening. Within each correlated group, we apply the elastic net to select variables and estim

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Li, Fuwei, Lifeng Lai, and Shuguang Cui. "On the Adversarial Robustness of LASSO Based Feature Selection." IEEE Transactions on Signal Processing 69 (2021): 5555–67. http://dx.doi.org/10.1109/tsp.2021.3115943.

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Zhang, Huaqing, Jian Wang, Zhanquan Sun, Jacek M. Zurada, and Nikhil R. Pal. "Feature Selection for Neural Networks Using Group Lasso Regularization." IEEE Transactions on Knowledge and Data Engineering 32, no. 4 (2020): 659–73. http://dx.doi.org/10.1109/tkde.2019.2893266.

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Huang, Weihai, Xinyue Liu, Weize Yang, Yihua Li, Qiyan Sun, and Xiangzeng Kong. "Motor Imagery EEG Signal Classification Using Distinctive Feature Fusion with Adaptive Structural LASSO." Sensors 24, no. 12 (2024): 3755. http://dx.doi.org/10.3390/s24123755.

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A motor imagery brain–computer interface connects the human brain and computers via electroencephalography (EEG). However, individual differences in the frequency ranges of brain activity during motor imagery tasks pose a challenge, limiting the manual feature extraction for motor imagery classification. To extract features that match specific subjects, we proposed a novel motor imagery classification model using distinctive feature fusion with adaptive structural LASSO. Specifically, we extracted spatial domain features from overlapping and multi-scale sub-bands of EEG signals and mined discr

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Skwirz, Wojciech. "Feature selection methods for Cox proportional hazards model. Comparative study for financial and medical survival data." Bank i Kredyt Vol. 56, no. 01 (2025): 113–38. https://doi.org/10.5604/01.3001.0054.9612.

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This study compares Cox proportional hazards models across medical and financial datasets built using various feature selection techniques. In this analysis 8 feature selection techniques (3 variants of forward selection, 2 variants of a selection based on principal component analysis, selection based on random survival forest, best subset selection and a selection based on a LASSO regularization) were tested across 22 multidimensional datasets (2 financial and 22 medical). The resulting Cox models were compared based on a concordance index. The main hypothesis of this study stating that the L

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Rygh, Tormod, Camilla Vaage, Sjur Westgaard, and Petter Eilif de Lange. "Inflation Forecasting: LSTM Networks vs. Traditional Models for Accurate Predictions." Journal of Risk and Financial Management 18, no. 7 (2025): 365. https://doi.org/10.3390/jrfm18070365.

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This study investigates the effectiveness of neural network models, particularly LSTM networks, in enhancing the accuracy of inflation forecasting. We compare LSTM models with traditional univariate time series models such as SARIMA and AR(p) models, as well as machine learning approaches like LASSO regression. To improve the standard LSTM model, we apply advanced feature selection techniques and introduce data augmentation using the MBB method. Our analysis reveals that LASSO-LSTM hybrid models generally outperform LSTM models utilizing PCA for feature selection, particularly in datasets with

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Khan, Mustafa Ahmed, Khalid Mahboob, Urooj Yousuf, Muhammad Ramzan, Muhammad Taha Shaikh, and Salman Akber. "Investigating the Role of LASSO in Feature Selection for Educational Data Mining (EDM) Applications." VFAST Transactions on Software Engineering 13, no. 2 (2025): 56–67. https://doi.org/10.21015/vtse.v13i2.2111.

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With the advent of digitalization, education-related activities have started generating massive amounts of data from various facets, such as student interaction, assessment, and learning management systems. Such vast amounts of data become suitable areas for Educational Data Mining (EDM) to reveal insights for actionable improvement in academic outcomes and personalized learning experiences. However, high dimensionality and the redundancy of the educational data also pose considerable threats to the accuracy, interpretability, and computational efficiency of modeling. Least Absolute Shrinkage

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Zubair, Iqbal Muhammad, Yung-Seop Lee, and Byunghoon Kim. "A New Permutation-Based Method for Ranking and Selecting Group Features in Multiclass Classification." Applied Sciences 14, no. 8 (2024): 3156. http://dx.doi.org/10.3390/app14083156.

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The selection of group features is a critical aspect in reducing model complexity by choosing the most essential group features, while eliminating the less significant ones. The existing group feature selection methods select a set of important group features, without providing the relative importance of all group features. Moreover, few methods consider the relative importance of group features in the selection process. This study introduces a permutation-based group feature selection approach specifically designed for high-dimensional multiclass datasets. Initially, the least absolute shrink

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Siti, Sarah Md Noh, Ibrahim Nurain, M. Mansor Mahayaudin, Azura Md Ghani Nor, and Yusoff Marina. "Hybrid embedded and filter feature selection methods in big dimension mammary cancer and prostatic cancer data." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3101–10. https://doi.org/10.11591/ijai.v13.i3.pp3101-3110.

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The feature selection method enhances machine learning performance by enhancing learning precision. Determining the optimal feature selection method for a given machine learning task involving big-dimension data is crucial. Therefore, the purpose of this study is to make a comparison of feature selection methods highlighting several filters (information gain, chi-square, ReliefF) and embedded (Lasso, Ridge) hybrid with logistic regression (LR). A sample size of n=100, 75 is chosen randomly, and the reduction features d=50, 22, and 10 are applied. The procedure for feature reduction makes use o

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Thiruvengadam, Kannan, Dadakhalandar Doddamani, and Rajendran Krishnan. "Performance of the Classical Model in Feature Selection Across Varying Database Sizes of Healthcare Data." International Journal of Statistics in Medical Research 13 (October 14, 2024): 228–37. http://dx.doi.org/10.6000/1929-6029.2024.13.21.

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Machine learning is increasingly being applied to medical research, particularly in selecting predictive modelling variables. By identifying relevant variables, researchers can improve model accuracy and reliability, leading to better clinical decisions and reduced overfitting. Efficient utilization of resources and the validity of medical research findings depend on selecting the right variables. However, few studies compare the performance of classical and modern methods for selecting characteristics in health datasets, highlighting the need for a critical evaluation to choose the most suita

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He, Yuxin, Yang Zhao, and Kwok Leung Tsui. "Exploring influencing factors on transit ridership from a local perspective." Smart and Resilient Transport 1, no. 1 (2019): 2–16. http://dx.doi.org/10.1108/srt-06-2019-0002.

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Purpose Exploring the influencing factors on urban rail transit (URT) ridership is vital for travel demand estimation and urban resources planning. Among various existing ridership modeling methods, direct demand model with ordinary least square (OLS) multiple regression as a representative has considerable advantages over the traditional four-step model. Nevertheless, OLS multiple regression neglects spatial instability and spatial heterogeneity from the magnitude of the coefficients across the urban area. This paper aims to focus on modeling and analyzing the factors influencing metro riders

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Wang, Er, Tianbao Huang, Zhi Liu, et al. "Improving Forest Above-Ground Biomass Estimation Accuracy Using Multi-Source Remote Sensing and Optimized Least Absolute Shrinkage and Selection Operator Variable Selection Method." Remote Sensing 16, no. 23 (2024): 4497. https://doi.org/10.3390/rs16234497.

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Estimation of forest above-ground biomass (AGB) using multi-source remote sensing data is an important method to improve the accuracy of the estimate. However, selecting remote sensing factors that can effectively improve the accuracy of forest AGB estimation from a large amount of data is a challenge when the sample size is small. In this regard, the Least Absolute Shrinkage and Selection Operator (Lasso) has advantages for extensive redundant variables but still has some drawbacks. To address this, the study introduces two Least Absolute Shrinkage and Selection Operator Lasso-based variable

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Likhachov, D. S., M. I. Vashkevich, N. A. Petrovsky, and E. S. Azarov. "Combined Method for Informative Feature Selection for Speech Pathology Detection." Doklady BGUIR 21, no. 4 (2023): 110–17. http://dx.doi.org/10.35596/1729-7648-2023-21-4-110-117.

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The task of detecting vocal abnormalities is characterized by a small amount of available data for training, as a consequence of which classification systems that use low-dimensional data are the most relevant. We propose to use LASSO (least absolute shrinkage and selection operator) and BSS (backward stepwise selection) methods together to select the most significant features for the detection of vocal pathologies, in particular amyotrophic lateral sclerosis. Features based on fine-frequency cepstral coefficients, traditionally used in speech signal processing, and features based on discrete

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Muniasamy, Anandhavalli, Arshiya Begum, Asfia Sabahath, Humara yaqub, and Gauthaman Karunakaran. "Coronary heart disease classification using deep learning approach with feature selection for improved accuracy." Technology and Health Care 32, no. 3 (2024): 1991–2007. https://doi.org/10.3233/thc-231807.

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BACKGROUND: Coronary heart disease (CHD) is one of the deadliest diseases and a risk prediction model for cardiovascular conditions is needed. Due to the huge number of features that lead to heart problems, it is often difficult for an expert to evaluate these huge features into account. So, there is a need of appropriate feature selection for the given CHD dataset. For early CHD detection, deep learning modes (DL) show promising results in the existing studies. OBJECTIVE: This study aimed to develop a deep convolution neural network (CNN) model for classification with a selected number of eff

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Luo, Shan, and Zehua Chen. "Sequential Lasso Cum EBIC for Feature Selection With Ultra-High Dimensional Feature Space." Journal of the American Statistical Association 109, no. 507 (2014): 1229–40. http://dx.doi.org/10.1080/01621459.2013.877275.

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Sen Puliparambil, Bhavithry, Jabed H. Tomal, and Yan Yan. "A Novel Algorithm for Feature Selection Using Penalized Regression with Applications to Single-Cell RNA Sequencing Data." Biology 11, no. 10 (2022): 1495. http://dx.doi.org/10.3390/biology11101495.

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With the emergence of single-cell RNA sequencing (scRNA-seq) technology, scientists are able to examine gene expression at single-cell resolution. Analysis of scRNA-seq data has its own challenges, which stem from its high dimensionality. The method of machine learning comes with the potential of gene (feature) selection from the high-dimensional scRNA-seq data. Even though there exist multiple machine learning methods that appear to be suitable for feature selection, such as penalized regression, there is no rigorous comparison of their performances across data sets, where each poses its own

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Mohammad Rasel Mahmud, Al Shahriar Uddin Khondakar Pranta, Anamul Haque Sakib, Abdullah Al Sakib, and Md Ismail Hossain Siddiqui. "Robust feature selection for improved sleep stage classification." International Journal of Science and Research Archive 15, no. 1 (2025): 1790–97. https://doi.org/10.30574/ijsra.2025.15.1.1160.

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Effective sleep stage classification requires identifying discriminative EEG features that remain consistent across different subjects. This study proposes an ensemble feature selection framework for robust sleep stage classification using the Physionet EEG dataset. We extract 40+ features from time and frequency domains, then employ multiple selection techniques including mutual information, recursive feature elimination, and Lasso regularization. Our ensemble approach ranks features based on selection frequency across methods and cross-validation folds, identifying a minimal effective featur

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Zhang, Xin, Tinghua Wang, and Zhiyong Lai. "A Feature-Weighted Support Vector Regression Machine Based on Hilbert–Schmidt Independence Criterion Least Absolute Shrinkage and Selection Operator." Information 15, no. 10 (2024): 639. http://dx.doi.org/10.3390/info15100639.

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Support vector regression (SVR) is a powerful kernel-based regression prediction algorithm that performs excellently in various application scenarios. However, for real-world data, the general SVR often fails to achieve good predictive performance due to its inability to assess feature contribution accurately. Feature weighting is a suitable solution to address this issue, applying correlation measurement methods to obtain reasonable weights for features based on their contributions to the output. In this paper, based on the idea of a Hilbert–Schmidt independence criterion least absolute shrin

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Jayasinghe, W. J. M. Lakmini Prarthana, Ravinesh C. Deo, Nawin Raj, et al. "Forecasting Multi-Step Soil Moisture with Three-Phase Hybrid Wavelet-Least Absolute Shrinkage Selection Operator-Long Short-Term Memory Network (moDWT-Lasso-LSTM) Model." Water 16, no. 21 (2024): 3133. http://dx.doi.org/10.3390/w16213133.

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To develop agricultural risk management strategies, the early identification of water deficits during the growing cycle is critical. This research proposes a deep learning hybrid approach for multi-step soil moisture forecasting in the Bundaberg region in Queensland, Australia, with predictions made for 1-day, 14-day, and 30-day, intervals. The model integrates Geospatial Interactive Online Visualization and Analysis Infrastructure (Giovanni) satellite data with ground observations. Due to the periodicity, transience, and trends in soil moisture of the top layer, time series datasets were comp

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Sun, Zhoubao, Kai Zhang, Yan Zhu, Yanzhe Ji, and Pingping Wu. "Unlocking Visual Attraction: The Subtle Relationship between Image Features and Attractiveness." Mathematics 12, no. 7 (2024): 1005. http://dx.doi.org/10.3390/math12071005.

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The interest of advertising designers and operators in crafting appealing images is steadily increasing. With a primary focus on image attractiveness, this study endeavors to uncover the correlation between image features and attractiveness. The ultimate objective is to enhance the accuracy of predicting image attractiveness to achieve visually captivating effects. The experimental subjects encompass images sourced from the Shutterstock website, and the correlation between image features and attractiveness is analyzed through image attractiveness scores. In our experiments, we extracted tradit

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Rochayani, Masithoh Yessi, Umu Sa'adah, and Ani Budi Astuti. "Two-stage Gene Selection and Classification for a High-Dimensional Microarray Data." Jurnal Online Informatika 5, no. 1 (2020): 9–18. http://dx.doi.org/10.15575/join.v5i1.569.

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Microarray technology has provided benefits for cancer diagnosis and classification. However, classifying cancer using microarray data is confronted with difficulty since the dataset has high dimensions. One strategy for dealing with the dimensionality problem is to make a feature selection before modeling. Lasso is a common regularization method to reduce the number of features or predictors. However, Lasso remains too many features at the optimum regularization parameter. Therefore, feature selection can be continued to the second stage. We proposed Classification and Regression Tree (CART)

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Md Noh, Siti Sarah, Nurain Ibrahim, Mahayaudin M. Mansor, Nor Azura Md Ghani, and Marina Yusoff. "Hybrid embedded and filter feature selection methods in big-dimension mammary cancer and prostatic cancer data." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3101. http://dx.doi.org/10.11591/ijai.v13.i3.pp3101-3110.

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<p>The feature selection method enhances machine learning performance by enhancing learning precision. Determining the optimal feature selection method for a given machine learning task involving big-dimension data is crucial. Therefore, the purpose of this study is to make a comparison of feature selection methods highlighting several filters (information gain, chi-square, ReliefF) and embedded (Lasso, Ridge) hybrid with logistic regression (LR). A sample size of n=100, 75 is chosen randomly, and the reduction features d=50, 22, and 10 are applied. The procedure for feature reduction ma

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Bel, Felcia, and Sabeen Selvaraj. "Hybrid optimal feature selection approach for internet of things based medical data analysis for prognosis." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 2011. http://dx.doi.org/10.11591/ijai.v13.i2.pp2011-2018.

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<p>Healthcare is very important application domain in internet of things (IoT). The aim is to provide a novel combined feature selection (FS) methods like univariate (UV) with tree-based methods (TB), recursive feature elimination (RFE) with least absolute shrinkage selection operator (LASSO), mutual information (MI) with genetic algorithm (GA) and embedded methods (EM) with univariate has been applied to internet of medical things (IoMT)based heart disease dataset. The well-suited machine learning algorithms for IoT medical data are logistic regression (LR) and support vector machine (S

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