{"title":"一个快速的HITON_PC算法","authors":"Wei Yang","doi":"10.1109/CIS.2010.17","DOIUrl":null,"url":null,"abstract":"The HITON_PC algorithm which is a state-of-the-art local causal discovery algorithm can deal with a dataset with a very small sample-to-variable ratio efficiently. But it cannot perform inefficiently on a dataset with a very large sample. To address this problem, a fast HITON_PC algorithm is presented which uses a new yet simple search strategy from high order to low order to improve the efficiency of HITON-PC. Experimental results show our fast HITON_PC outperforms the HITON_PC algorithm. Moreover, we also apply the new search strategy to MMPC algorithm. Our method also is superior to MMPC.","PeriodicalId":420515,"journal":{"name":"2010 International Conference on Computational Intelligence and Security","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Fast HITON_PC Algorithm\",\"authors\":\"Wei Yang\",\"doi\":\"10.1109/CIS.2010.17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The HITON_PC algorithm which is a state-of-the-art local causal discovery algorithm can deal with a dataset with a very small sample-to-variable ratio efficiently. But it cannot perform inefficiently on a dataset with a very large sample. To address this problem, a fast HITON_PC algorithm is presented which uses a new yet simple search strategy from high order to low order to improve the efficiency of HITON-PC. Experimental results show our fast HITON_PC outperforms the HITON_PC algorithm. Moreover, we also apply the new search strategy to MMPC algorithm. Our method also is superior to MMPC.\",\"PeriodicalId\":420515,\"journal\":{\"name\":\"2010 International Conference on Computational Intelligence and Security\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Computational Intelligence and Security\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIS.2010.17\",\"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 International Conference on Computational Intelligence and Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIS.2010.17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The HITON_PC algorithm which is a state-of-the-art local causal discovery algorithm can deal with a dataset with a very small sample-to-variable ratio efficiently. But it cannot perform inefficiently on a dataset with a very large sample. To address this problem, a fast HITON_PC algorithm is presented which uses a new yet simple search strategy from high order to low order to improve the efficiency of HITON-PC. Experimental results show our fast HITON_PC outperforms the HITON_PC algorithm. Moreover, we also apply the new search strategy to MMPC algorithm. Our method also is superior to MMPC.