{"title":"A Multi-objective Feature Selection Method Considering the Interaction Between Features","authors":"","doi":"10.1007/s10796-024-10481-2","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Feature selection (FS) is one of the major tasks in data cleansing step in machine learning. However, multi-objective FS is more challenging because it tries to optimize two conflicting objectives, namely minimizing the feature set and classification error. In this way, evolutionary algorithms are promising solutions aimed to obtain more reliable Pareto fronts. However, unfortunately they suffer from consuming much time due to exploration in a large search space. Another issue encountered in multi-objective FS approaches is related to the correlation between features. This challenge arises because choosing such features reduces the performance of the classification. To address these challenges, we introduce a multi-objective FS approach that makes several significant contributions. First, the proposed method deals with the correlation between features through a novel probability structure. Secondly, it relies on the Pareto Archived Evolution Strategy (PAES) method, which offers many advantages, including simplicity and its ability to explore the solution space at an acceptable speed. We enhance the PAES structure in a manner that promotes the intelligent generation of offsprings. Consequently, our proposed approach benefits from the introduced probability structure to generate more promising offspring. Lastly, it incorporates a novel strategy to guide the algorithm to find the optimal subset throughout the evolutionary process. The obtained results on real-world datasets reveal a substantial enhancement in the quality of the final solutions.</p>","PeriodicalId":13610,"journal":{"name":"Information Systems Frontiers","volume":"1 1","pages":""},"PeriodicalIF":6.9000,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Systems Frontiers","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10796-024-10481-2","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Feature selection (FS) is one of the major tasks in data cleansing step in machine learning. However, multi-objective FS is more challenging because it tries to optimize two conflicting objectives, namely minimizing the feature set and classification error. In this way, evolutionary algorithms are promising solutions aimed to obtain more reliable Pareto fronts. However, unfortunately they suffer from consuming much time due to exploration in a large search space. Another issue encountered in multi-objective FS approaches is related to the correlation between features. This challenge arises because choosing such features reduces the performance of the classification. To address these challenges, we introduce a multi-objective FS approach that makes several significant contributions. First, the proposed method deals with the correlation between features through a novel probability structure. Secondly, it relies on the Pareto Archived Evolution Strategy (PAES) method, which offers many advantages, including simplicity and its ability to explore the solution space at an acceptable speed. We enhance the PAES structure in a manner that promotes the intelligent generation of offsprings. Consequently, our proposed approach benefits from the introduced probability structure to generate more promising offspring. Lastly, it incorporates a novel strategy to guide the algorithm to find the optimal subset throughout the evolutionary process. The obtained results on real-world datasets reveal a substantial enhancement in the quality of the final solutions.
期刊介绍：
The interdisciplinary interfaces of Information Systems (IS) are fast emerging as defining areas of research and development in IS. These developments are largely due to the transformation of Information Technology (IT) towards networked worlds and its effects on global communications and economies. While these developments are shaping the way information is used in all forms of human enterprise, they are also setting the tone and pace of information systems of the future. The major advances in IT such as client/server systems, the Internet and the desktop/multimedia computing revolution, for example, have led to numerous important vistas of research and development with considerable practical impact and academic significance. While the industry seeks to develop high performance IS/IT solutions to a variety of contemporary information support needs, academia looks to extend the reach of IS technology into new application domains. Information Systems Frontiers (ISF) aims to provide a common forum of dissemination of frontline industrial developments of substantial academic value and pioneering academic research of significant practical impact.
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