{"title":"基于Lasso选择的集成学习算法","authors":"K. Chen, Yang Jin","doi":"10.1109/ICICISYS.2010.5658515","DOIUrl":null,"url":null,"abstract":"Ensemble learning, especially selective ensemble learning is now becoming more and more popular in the field of machine learning. This paper introduces a new ensemble algorithm, named Lasso-Bagging Trees ensemble algorithm. This algorithm is in order to improve the whole learning ability, which is a combination of tree predictors and this method chooses and ensembles trees based on the shrinkage estimation of lasso technology. Compared with a series of other learning algorithms, it demonstrates better generalization ability and higher efficiency.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An ensemble learning algorithm based on Lasso selection\",\"authors\":\"K. Chen, Yang Jin\",\"doi\":\"10.1109/ICICISYS.2010.5658515\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ensemble learning, especially selective ensemble learning is now becoming more and more popular in the field of machine learning. This paper introduces a new ensemble algorithm, named Lasso-Bagging Trees ensemble algorithm. This algorithm is in order to improve the whole learning ability, which is a combination of tree predictors and this method chooses and ensembles trees based on the shrinkage estimation of lasso technology. Compared with a series of other learning algorithms, it demonstrates better generalization ability and higher efficiency.\",\"PeriodicalId\":339711,\"journal\":{\"name\":\"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICISYS.2010.5658515\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICISYS.2010.5658515","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An ensemble learning algorithm based on Lasso selection
Ensemble learning, especially selective ensemble learning is now becoming more and more popular in the field of machine learning. This paper introduces a new ensemble algorithm, named Lasso-Bagging Trees ensemble algorithm. This algorithm is in order to improve the whole learning ability, which is a combination of tree predictors and this method chooses and ensembles trees based on the shrinkage estimation of lasso technology. Compared with a series of other learning algorithms, it demonstrates better generalization ability and higher efficiency.