{"title":"特征选择问题的自然启发元启发式优化研究进展综述","authors":"Maha Nssibi , Ghaith Manita , Ouajdi Korbaa","doi":"10.1016/j.cosrev.2023.100559","DOIUrl":null,"url":null,"abstract":"<div><p>The main objective of feature selection is to improve learning performance by selecting concise and informative feature subsets, which presents a challenging task for machine learning<span> or pattern recognition applications due to the large and complex search space involved. This paper provides an in-depth examination of nature-inspired metaheuristic methods for the feature selection problem, with a focus on representation and search algorithms, as they have drawn significant interest from the feature selection community due to their potential for global search and simplicity. An analysis of various advanced approach types, along with their advantages and disadvantages, is presented in this study, with the goal of highlighting important issues and unanswered questions in the literature. The article provides advice for conducting future research more effectively to benefit this field of study, including guidance on identifying appropriate approaches to use in different scenarios.</span></p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"49 ","pages":"Article 100559"},"PeriodicalIF":13.3000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey\",\"authors\":\"Maha Nssibi , Ghaith Manita , Ouajdi Korbaa\",\"doi\":\"10.1016/j.cosrev.2023.100559\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The main objective of feature selection is to improve learning performance by selecting concise and informative feature subsets, which presents a challenging task for machine learning<span> or pattern recognition applications due to the large and complex search space involved. This paper provides an in-depth examination of nature-inspired metaheuristic methods for the feature selection problem, with a focus on representation and search algorithms, as they have drawn significant interest from the feature selection community due to their potential for global search and simplicity. An analysis of various advanced approach types, along with their advantages and disadvantages, is presented in this study, with the goal of highlighting important issues and unanswered questions in the literature. The article provides advice for conducting future research more effectively to benefit this field of study, including guidance on identifying appropriate approaches to use in different scenarios.</span></p></div>\",\"PeriodicalId\":48633,\"journal\":{\"name\":\"Computer Science Review\",\"volume\":\"49 \",\"pages\":\"Article 100559\"},\"PeriodicalIF\":13.3000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Science Review\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1574013723000266\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Science Review","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1574013723000266","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey
The main objective of feature selection is to improve learning performance by selecting concise and informative feature subsets, which presents a challenging task for machine learning or pattern recognition applications due to the large and complex search space involved. This paper provides an in-depth examination of nature-inspired metaheuristic methods for the feature selection problem, with a focus on representation and search algorithms, as they have drawn significant interest from the feature selection community due to their potential for global search and simplicity. An analysis of various advanced approach types, along with their advantages and disadvantages, is presented in this study, with the goal of highlighting important issues and unanswered questions in the literature. The article provides advice for conducting future research more effectively to benefit this field of study, including guidance on identifying appropriate approaches to use in different scenarios.
期刊介绍:
Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.