Marc Jermaine Pontiveros, Geoffrey A. Solano, J. Diaz, Jaime D. L. Caro
{"title":"Feature Subset Selection Using Genetic Algorithm with Aggressive Mutation for Classification Problem","authors":"Marc Jermaine Pontiveros, Geoffrey A. Solano, J. Diaz, Jaime D. L. Caro","doi":"10.1109/TENCON54134.2021.9707450","DOIUrl":null,"url":null,"abstract":"In this work, the algorithmic approaches to Feature Subset Selection (FSS) are reviewed. FSS is the technique of selecting a subset of relevant features for building parsimonious models. One successful algorithm in selecting relevant features in Brain-Computing Interface is the Genetic Algorithm with Aggressive Mutation (GAAM). We implemented a scikit-learn compatible library for GAAM and determined its applicability with classification tasks in general. Identifying relevant features in a predictive modeling task improves the interpretability of the model, reduces its complexity and the time requirement for training.","PeriodicalId":405859,"journal":{"name":"TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TENCON54134.2021.9707450","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this work, the algorithmic approaches to Feature Subset Selection (FSS) are reviewed. FSS is the technique of selecting a subset of relevant features for building parsimonious models. One successful algorithm in selecting relevant features in Brain-Computing Interface is the Genetic Algorithm with Aggressive Mutation (GAAM). We implemented a scikit-learn compatible library for GAAM and determined its applicability with classification tasks in general. Identifying relevant features in a predictive modeling task improves the interpretability of the model, reduces its complexity and the time requirement for training.