【摘要】 通过分析基于关联规则的文本分类,发现在保持分类规则对正例样本正确分类的同时减少对反例样本的错误分类可以提高分类的精确度.基于否定选择算法的思想提出了分类规则修正策略,用反例样本集合对分类规则进行耐受,从分类规则错误判别的反例样本中再产生规则,与原来的规则组成新规则,称为增强关联规则.基于修正策略产生的增强关联规则可以大幅度地减少对反例样本的错误分类,从而提高分类的精确度.通过形式化证明和实验,分类规则修正策略的有效性得到验证.更多还原

【Abstract】 Text classification is an important field in data mining and machine learning. In recent years, the use of association rules for text categorization has attracted great interest and a variety of useful methods have been developed. These works focus on how to generate classification rules and then pick rules to build a high accuracy classifier. By analyzing association-rule based text classification, an observation may be obtained that decreasing error classification for negative samples may improve classification accuracy while keeping categorizing positive samples unchanged. Inspired by negative selection algorithm, the authors propose a classification rule revising strategy to implement the above observation. First, a new rule, called negative rule, is generated by mining frequent item sets on negative samples that are error categorized by a classification rule. Then the classification rule is combined with its negative rules to generate an enhanced association rule. The enhanced association rules can dramatically decrease error categorization for negative samples, and therefore classification accuracy is improved. Experiments are conducted on a real Web pages dataset. Compared with text classification algorithms (CMAR, S-EM and NB), the rule revising strategy may further improve classification accuracy. The utility and feasibility of the revising rule strategy are also demonstrated by formalization proof.更多还原