{"title":"u曲线优化的改进分支定界算法","authors":"E. Atashpaz-Gargari, U. Braga-Neto, E. Dougherty","doi":"10.1109/GENSIPS.2013.6735948","DOIUrl":null,"url":null,"abstract":"The U-curve branch-and-bound algorithm for optimization was introduced recently by Ris and collaborators. In this paper we introduce an improved algorithm for finding the optimal set of features based on the U-curve assumption. Synthetic experiments are used to asses the performance of the proposed algorithm, and compare it to exhaustive search and the original algorithm. The results show that the modified U-curve BB algorithm makes fewer evaluations and is more robust than the original algorithm.","PeriodicalId":336511,"journal":{"name":"2013 IEEE International Workshop on Genomic Signal Processing and Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Improved branch-and-bound algorithm for U-curve optimization\",\"authors\":\"E. Atashpaz-Gargari, U. Braga-Neto, E. Dougherty\",\"doi\":\"10.1109/GENSIPS.2013.6735948\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The U-curve branch-and-bound algorithm for optimization was introduced recently by Ris and collaborators. In this paper we introduce an improved algorithm for finding the optimal set of features based on the U-curve assumption. Synthetic experiments are used to asses the performance of the proposed algorithm, and compare it to exhaustive search and the original algorithm. The results show that the modified U-curve BB algorithm makes fewer evaluations and is more robust than the original algorithm.\",\"PeriodicalId\":336511,\"journal\":{\"name\":\"2013 IEEE International Workshop on Genomic Signal Processing and Statistics\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE International Workshop on Genomic Signal Processing and Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GENSIPS.2013.6735948\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Workshop on Genomic Signal Processing and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GENSIPS.2013.6735948","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improved branch-and-bound algorithm for U-curve optimization
The U-curve branch-and-bound algorithm for optimization was introduced recently by Ris and collaborators. In this paper we introduce an improved algorithm for finding the optimal set of features based on the U-curve assumption. Synthetic experiments are used to asses the performance of the proposed algorithm, and compare it to exhaustive search and the original algorithm. The results show that the modified U-curve BB algorithm makes fewer evaluations and is more robust than the original algorithm.