{"title":"基因微阵列数据分析的云并行遗传算法","authors":"R. A. B. Palomino, L. Liang","doi":"10.1109/ICTAI.2011.160","DOIUrl":null,"url":null,"abstract":"In this paper we propose FM-PGA, a MapReduce-based hybrid of FM-test and Parallel Genetic Algorithm (PGA), to analyze gene micro array data on cloud computing platform. We investigate the performance of FM-PGA on real-world micro array data and compare it with FM-GA and MapReduce PGA. The experimental results confirm that the genes selected by FM-PGA achieve comparable classification accuracy while the time to achieve that accuracy is significantly reduced.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Cloud Parallel Genetic Algorithm for Gene Microarray Data Analysis\",\"authors\":\"R. A. B. Palomino, L. Liang\",\"doi\":\"10.1109/ICTAI.2011.160\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we propose FM-PGA, a MapReduce-based hybrid of FM-test and Parallel Genetic Algorithm (PGA), to analyze gene micro array data on cloud computing platform. We investigate the performance of FM-PGA on real-world micro array data and compare it with FM-GA and MapReduce PGA. The experimental results confirm that the genes selected by FM-PGA achieve comparable classification accuracy while the time to achieve that accuracy is significantly reduced.\",\"PeriodicalId\":332661,\"journal\":{\"name\":\"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTAI.2011.160\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTAI.2011.160","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cloud Parallel Genetic Algorithm for Gene Microarray Data Analysis
In this paper we propose FM-PGA, a MapReduce-based hybrid of FM-test and Parallel Genetic Algorithm (PGA), to analyze gene micro array data on cloud computing platform. We investigate the performance of FM-PGA on real-world micro array data and compare it with FM-GA and MapReduce PGA. The experimental results confirm that the genes selected by FM-PGA achieve comparable classification accuracy while the time to achieve that accuracy is significantly reduced.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
