{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-feature-selection-based-on-perturbation","title":"A Feature Selection Based on Perturbation Theory","arxiv_id":"1902.09938","date":"2019-02-26","proceeding":null,"authors":["Javad Rahimipour Anaraki","Hamid Usefi"],"abstract":"Consider a supervised dataset $D=[A\\mid \\textbf{b}]$, where $\\textbf{b}$ is\nthe outcome column, rows of $D$ correspond to observations, and columns of $A$\nare the features of the dataset. A central problem in machine learning and\npattern recognition is to select the most important features from $D$ to be\nable to predict the outcome. In this paper, we provide a new feature selection\nmethod where we use perturbation theory to detect correlations between\nfeatures. We solve $AX=\\textbf{b}$ using the method of least squares and\nsingular value decomposition of $A$. In practical applications, such as in\nbioinformatics, the number of rows of $A$ (observations) are much less than the\nnumber of columns of $A$ (features). So we are dealing with singular matrices\nwith big condition numbers. Although it is known that the solutions of least\nsquare problems in singular case are very sensitive to perturbations in $A$,\nour novel approach in this paper is to prove that the correlations between\nfeatures can be detected by applying perturbations to $A$. The effectiveness of\nour method is verified by performing a series of comparisons with conventional\nand novel feature selection methods in the literature. It is demonstrated that\nin most situations, our method chooses considerably less number of features\nwhile attaining or exceeding the accuracy of the other methods.","url_abs":"http://arxiv.org/abs/1902.09938v1","url_pdf":"http://arxiv.org/pdf/1902.09938v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-feature-selection-based-on-perturbation","repo_url":"https://github.com/jracp/PerturbationFeatureSelection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-feature-selection-based-on-perturbation","repo_url":"https://github.com/jranaraki/PerturbationFeatureSelection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}