{"title":"机器学习中的特征选择和特征选择稳定性综述","authors":"Mustafa Büyükkeçeci̇, M. C. Okur","doi":"10.35378/gujs.993763","DOIUrl":null,"url":null,"abstract":"Feature selection is a data preprocessing method used to reduce the number of features in the datasets. Feature selection techniques search the entire feature space to find an optimal feature set that is free of redundant and irrelevant features. Reducing the dimensionality of a dataset by removing redundant and irrelevant features has a pivotal role in improving the performance, i.e., accuracy, of inductive learners and building simple models. Thus, feature selection is an imperative task of machine learning. The apparent need for feature selection raised considerable interest and became an important research topic in a wide range of fields, including bioinformatics, text classification, image recognition, and computer vision. As a result, a large pool of feature selection methods has been proposed, and a considerable amount of literature has been published on feature selection. The quality of feature selection algorithms is measured not only by the performance of the features they prefer but also by their stability. Therefore, this study focuses on two topics: feature selection and feature selection stability. In the pages that follow, general concepts and methods of feature selection are discussed and then an overview of feature selection stability and stability measures are given.","PeriodicalId":12615,"journal":{"name":"gazi university journal of science","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Comprehensive Review of Feature Selection and Feature Selection Stability in Machine Learning\",\"authors\":\"Mustafa Büyükkeçeci̇, M. C. Okur\",\"doi\":\"10.35378/gujs.993763\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Feature selection is a data preprocessing method used to reduce the number of features in the datasets. Feature selection techniques search the entire feature space to find an optimal feature set that is free of redundant and irrelevant features. Reducing the dimensionality of a dataset by removing redundant and irrelevant features has a pivotal role in improving the performance, i.e., accuracy, of inductive learners and building simple models. Thus, feature selection is an imperative task of machine learning. The apparent need for feature selection raised considerable interest and became an important research topic in a wide range of fields, including bioinformatics, text classification, image recognition, and computer vision. As a result, a large pool of feature selection methods has been proposed, and a considerable amount of literature has been published on feature selection. The quality of feature selection algorithms is measured not only by the performance of the features they prefer but also by their stability. Therefore, this study focuses on two topics: feature selection and feature selection stability. In the pages that follow, general concepts and methods of feature selection are discussed and then an overview of feature selection stability and stability measures are given.\",\"PeriodicalId\":12615,\"journal\":{\"name\":\"gazi university journal of science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2022-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"gazi university journal of science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.35378/gujs.993763\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"gazi university journal of science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35378/gujs.993763","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
A Comprehensive Review of Feature Selection and Feature Selection Stability in Machine Learning
Feature selection is a data preprocessing method used to reduce the number of features in the datasets. Feature selection techniques search the entire feature space to find an optimal feature set that is free of redundant and irrelevant features. Reducing the dimensionality of a dataset by removing redundant and irrelevant features has a pivotal role in improving the performance, i.e., accuracy, of inductive learners and building simple models. Thus, feature selection is an imperative task of machine learning. The apparent need for feature selection raised considerable interest and became an important research topic in a wide range of fields, including bioinformatics, text classification, image recognition, and computer vision. As a result, a large pool of feature selection methods has been proposed, and a considerable amount of literature has been published on feature selection. The quality of feature selection algorithms is measured not only by the performance of the features they prefer but also by their stability. Therefore, this study focuses on two topics: feature selection and feature selection stability. In the pages that follow, general concepts and methods of feature selection are discussed and then an overview of feature selection stability and stability measures are given.
期刊介绍:
The scope of the “Gazi University Journal of Science” comprises such as original research on all aspects of basic science, engineering and technology. Original research results, scientific reviews and short communication notes in various fields of science and technology are considered for publication. The publication language of the journal is English. Manuscripts previously published in another journal are not accepted. Manuscripts with a suitable balance of practice and theory are preferred. A review article is expected to give in-depth information and satisfying evaluation of a specific scientific or technologic subject, supported with an extensive list of sources. Short communication notes prepared by researchers who would like to share the first outcomes of their on-going, original research work are welcome.