%0 Journal Article
%T Rule Extraction Approach to Text Categorization Based on Multi-population Collaborative Optimization<br>基于多种群协同优化的文本分类规则抽取方法
%A LIU He
%A LIU Da-You
%A PEI Zhi-Li
%A GAO Ying
%A <br>刘赫
%A 刘大有
%A 裴志利
%A 高滢
%J 自动化学报
%D 2009
%I 
%X For the problem of rule extraction in text categorization, a novel rule extraction approach to text categorization based on multi-population collaborative optimization was proposed. Information entropy was applied to generation of initial populations and the multi-population collaborative optimization method was employed to evolve the current population in this proposed approach. The optimization efficiency of this approach was improved by the mutual competition and excellent individuals sharing mechanisms among populations. Experimental results have shown that the number of the rules extracted by this approach is small, and that the accuracy of these rules is high and the average length of them is short. Furthermore, the time of this approach is short and the speed of rule extraction through this approach is high. Therefore, this approach is suitable for large-scale data sets.
%K Rule extraction
%K text categorization
%K multi-population collaborative optimization
%K genetic algorithm
%K ant colony algorithm<br>规则抽取
%K 文本分类
%K 多种群协同优化
%K 遗传算法
%K 蚁群算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=0A24F289493B14554E24E41877C21D26&yid=DE12191FBD62783C&vid=6209D9E8050195F5&iid=F3090AE9B60B7ED1&sid=C0C56F7E9227DF7D&eid=CEEFA682892739FB&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=0