文章基本信息

标题：Fuzzy Weighted Associative Classifier based on Positive and Negative Rules
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作者：M. Kalpana Devi ; M. Usha Rani
期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2014
卷号：5
期号：4
页码：5400-5403
出版社：TechScience Publications
摘要：Construction of effective and accurate classifier is one of the challenges facing by the researchers. Many experiments have shown that Associative Classifier is significantly more accurate than the traditional classifiers. To classify the quantitative data, Fuzzy Associative Classifier was introduced and is also proved as an effective prediction model. Mining of negative association rules have gaining much attention among researchers recently, because, negative rules are as important as positive rules. This method has been recently introduced in the literature. In this paper we propose a new framework, Fuzzy Weighted Associative Classifier based on positive and negative rules. The main aim of this paper is to improve prediction accuracy
关键词：Associative Classifier; Fuzzy Weighted Associative;Classifier; Negative association rules