{"title":"使用竞争性群体优化算法特征选择的标签学习方法,以改进多标签分类算法","authors":"Lianhe Cui","doi":"10.1016/j.jksuci.2024.102083","DOIUrl":null,"url":null,"abstract":"<div><p>One of the crucial pre-processing stages in data mining and machine learning is feature selection, which is used to choose a subset of representative characteristics and decrease dimensions. By eliminating unnecessary and redundant features, feature selection can improve machine learning tasks’ accuracy. This work presents a novel multi-label classification (MLC) model utilizing a combination of stack regression (RR) and original label space transformation (IPLST) called RR-IPLST (original label space transformation-ridge regression). A novel embedded technique is implemented, utilizing competitive crowding optimizer (CSO) for multi-label feature selection. Particles are first created using this procedure, after which they are split into two equal groups and compete in pairs. The winners advance to the next iteration, while the losers pick up tips from the victors. At the conclusion of each iteration, the objective function for every particle is determined. A local search technique inspired by the gradient descent algorithm is used to find the local structure of the data, and half of the initial population is produced by the similarity between features and labels in order to boost the convergence rate. Ultimately, feature selection is carried out depending on the best particle. Six popular and sophisticated multi-label feature selection techniques are evaluated to see how well the suggested approach performs. According to the simulation results, the application of the suggested solution performs better than comparable techniques in terms of stability, accuracy, precision, convergence, error measurement, and other criteria that have been examined on various data sets. In 93.35% of cases, the test results demonstrate superiority over traditional algorithms.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824001721/pdfft?md5=bc950af29ef97e35d24c4f924eb4ca7d&pid=1-s2.0-S1319157824001721-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A label learning approach using competitive population optimization algorithm feature selection to improve multi-label classification algorithms\",\"authors\":\"Lianhe Cui\",\"doi\":\"10.1016/j.jksuci.2024.102083\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>One of the crucial pre-processing stages in data mining and machine learning is feature selection, which is used to choose a subset of representative characteristics and decrease dimensions. By eliminating unnecessary and redundant features, feature selection can improve machine learning tasks’ accuracy. This work presents a novel multi-label classification (MLC) model utilizing a combination of stack regression (RR) and original label space transformation (IPLST) called RR-IPLST (original label space transformation-ridge regression). A novel embedded technique is implemented, utilizing competitive crowding optimizer (CSO) for multi-label feature selection. Particles are first created using this procedure, after which they are split into two equal groups and compete in pairs. The winners advance to the next iteration, while the losers pick up tips from the victors. At the conclusion of each iteration, the objective function for every particle is determined. A local search technique inspired by the gradient descent algorithm is used to find the local structure of the data, and half of the initial population is produced by the similarity between features and labels in order to boost the convergence rate. Ultimately, feature selection is carried out depending on the best particle. Six popular and sophisticated multi-label feature selection techniques are evaluated to see how well the suggested approach performs. According to the simulation results, the application of the suggested solution performs better than comparable techniques in terms of stability, accuracy, precision, convergence, error measurement, and other criteria that have been examined on various data sets. In 93.35% of cases, the test results demonstrate superiority over traditional algorithms.</p></div>\",\"PeriodicalId\":48547,\"journal\":{\"name\":\"Journal of King Saud University-Computer and Information Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1319157824001721/pdfft?md5=bc950af29ef97e35d24c4f924eb4ca7d&pid=1-s2.0-S1319157824001721-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of King Saud University-Computer and Information Sciences\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1319157824001721\",\"RegionNum\":2,\"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":"Journal of King Saud University-Computer and Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1319157824001721","RegionNum":2,"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}
A label learning approach using competitive population optimization algorithm feature selection to improve multi-label classification algorithms
One of the crucial pre-processing stages in data mining and machine learning is feature selection, which is used to choose a subset of representative characteristics and decrease dimensions. By eliminating unnecessary and redundant features, feature selection can improve machine learning tasks’ accuracy. This work presents a novel multi-label classification (MLC) model utilizing a combination of stack regression (RR) and original label space transformation (IPLST) called RR-IPLST (original label space transformation-ridge regression). A novel embedded technique is implemented, utilizing competitive crowding optimizer (CSO) for multi-label feature selection. Particles are first created using this procedure, after which they are split into two equal groups and compete in pairs. The winners advance to the next iteration, while the losers pick up tips from the victors. At the conclusion of each iteration, the objective function for every particle is determined. A local search technique inspired by the gradient descent algorithm is used to find the local structure of the data, and half of the initial population is produced by the similarity between features and labels in order to boost the convergence rate. Ultimately, feature selection is carried out depending on the best particle. Six popular and sophisticated multi-label feature selection techniques are evaluated to see how well the suggested approach performs. According to the simulation results, the application of the suggested solution performs better than comparable techniques in terms of stability, accuracy, precision, convergence, error measurement, and other criteria that have been examined on various data sets. In 93.35% of cases, the test results demonstrate superiority over traditional algorithms.
期刊介绍:
In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.