Rule Extraction Approach to Text Categorization Based on Multi-population Collaborative Optimization
基于多种群协同优化的文本分类规则抽取方法

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.