C. Castiello, Corrado Mencar, M. Lucarelli, Franz Rothlauf
{"title":"通过遗传引导提高DC *的效率","authors":"C. Castiello, Corrado Mencar, M. Lucarelli, Franz Rothlauf","doi":"10.1109/FUZZ-IEEE.2017.8015585","DOIUrl":null,"url":null,"abstract":"DC∗ is a method for generating interpretable fuzzy information granules from pre-classified data. It is based on the subsequent application of LVQ1 for data compression and an ad-hoc procedure based on A∗ to represent data with the minimum number of fuzzy information granules satisfying some interpretability constraints. While being efficient in tackling several problems, the A∗ procedure included in DC∗ may happen to require a long computation time because the A∗ algorithm has exponential time complexity in the worst case. In this paper, we approach the problem of driving the search process of A∗ by suggesting a close-to-optimal solution that is produced through a Genetic Algorithm (GA). Experimental evaluations show that, by driving the A∗ algorithm embodied in DC∗ with a GA solution, the time required to perform data granulation can be reduced by at least 45% and up to 99%.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Efficiency improvement of DC∗ through a Genetic Guidance\",\"authors\":\"C. Castiello, Corrado Mencar, M. Lucarelli, Franz Rothlauf\",\"doi\":\"10.1109/FUZZ-IEEE.2017.8015585\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"DC∗ is a method for generating interpretable fuzzy information granules from pre-classified data. It is based on the subsequent application of LVQ1 for data compression and an ad-hoc procedure based on A∗ to represent data with the minimum number of fuzzy information granules satisfying some interpretability constraints. While being efficient in tackling several problems, the A∗ procedure included in DC∗ may happen to require a long computation time because the A∗ algorithm has exponential time complexity in the worst case. In this paper, we approach the problem of driving the search process of A∗ by suggesting a close-to-optimal solution that is produced through a Genetic Algorithm (GA). Experimental evaluations show that, by driving the A∗ algorithm embodied in DC∗ with a GA solution, the time required to perform data granulation can be reduced by at least 45% and up to 99%.\",\"PeriodicalId\":408343,\"journal\":{\"name\":\"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZ-IEEE.2017.8015585\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015585","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
摘要
DC *是一种从预分类数据中产生可解释模糊信息颗粒的方法。它基于LVQ1在数据压缩中的后续应用和基于A *的临时过程，以满足某些可解释性约束的模糊信息颗粒的最小数量来表示数据。虽然在处理一些问题时是有效的，但包含在DC *中的A∗过程可能需要很长的计算时间，因为A∗算法在最坏情况下具有指数级的时间复杂度。在这篇论文中，我们通过提出一个由遗传算法(GA)产生的接近最优解来解决驱动A *搜索过程的问题。实验评估表明，通过用GA解决方案驱动DC *中包含的A *算法，执行数据粒化所需的时间可以减少至少45%至99%。
Efficiency improvement of DC∗ through a Genetic Guidance
DC∗ is a method for generating interpretable fuzzy information granules from pre-classified data. It is based on the subsequent application of LVQ1 for data compression and an ad-hoc procedure based on A∗ to represent data with the minimum number of fuzzy information granules satisfying some interpretability constraints. While being efficient in tackling several problems, the A∗ procedure included in DC∗ may happen to require a long computation time because the A∗ algorithm has exponential time complexity in the worst case. In this paper, we approach the problem of driving the search process of A∗ by suggesting a close-to-optimal solution that is produced through a Genetic Algorithm (GA). Experimental evaluations show that, by driving the A∗ algorithm embodied in DC∗ with a GA solution, the time required to perform data granulation can be reduced by at least 45% and up to 99%.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
