An Empirical Comparison of Rule Sets Induced by LERS and Probabilistic Rough Classification

Autor:	Yiyu Yao, Shantan R. Marepally, Jerzy W. Grzymala-Busse
Rok vydání:	2010
Předmět:	Empirical comparison business.industry Rule induction Rule sets Probabilistic logic Word error rate Pattern recognition computer.software_genre Rough set Artificial intelligence Data mining business Decision table computer Probabilistic approximations Mathematics
Zdroj:	Rough Sets and Current Trends in Computing ISBN: 9783642135286 RSCTC Rough Sets and Intelligent Systems-Professor Zdzisław Pawlak in Memoriam ISBN: 9783642303432
DOI:	10.1007/978-3-642-13529-3_63
Popis:	In this paper we present results of an experimental comparison (in terms of an error rate) of rule sets induced by the LERS data mining system with rule sets induced using the probabilistic rough classification (PRC). As follows from our experiments, the performance of LERS (possible rules) is significantly better than the best rule sets induced by PRC with any threshold (two-tailed test, 5% significance level). Additionally, the LERS possible rule approach to rule induction is significantly better than the LERS certain rule approach (two-tailed test, 5% significance level).
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::69d6848380d9d5120e612a0f3aa343c3 https://doi.org/10.1007/978-3-642-13529-3_63 Zobrazit plný text záznamu