【摘要】 提出了一种有效的用于数据挖掘分类任务的方法———组织协同进化分类算法 (OrganizationalCoEvolution aryalgorithmforClassification ,OCEC) ,与现有遗传分类方法的运行机制不同 ,它的进化操作直接作用于数据而不是规则 ,进化结束后再从各组织中提取规则 ,这样有利于避免在进化过程中产生无意义的规则 .提出了三种组织进化算子———增减算子、交换算子与合并算子和一种组织选择机制 ,给出了属性重要度的进化方式并基于此定义了组织适应度 .作者将算法用于UCI数据集 ,并与现有的基于遗传和非遗传的分类方法进行了比较 .实验结果表明该文方法获得了更高的预测准确率 ,产生了更小的规则集 ;对同一数据集进行k 次交叉验证 ,其预测准确率的波动较小 ,因此本文算法具有更加稳定的性能更多还原

【Abstract】 A novel classification method for data mining, Organizational CoEvolutionary algorithm for Classification (OCEC), is proposed in this paper, which is different from the GA based classification methods available. The evolutionary operations of OCEC do not act on rules, but on the given data directly, and rules are extracted from the final evolutionary results, which can avoid generating meaningless rules during evolutionary process. Three evolutionary operators, add and subtract operator, exchange operator and unite operator, and a selection mechanism are developed for organizations. The fitness of organization is then defined based on the importance of attributes, which are determined during evolution. OCEC is compared to other GA based and non GA based classification algorithms on some benchmark datasets from the UCI machine learning repository. Results show the proposed algorithm can achieve higher predicting accuracy and a smaller number of rules. In addition, its performance is more stable in that its predicting accuracy fluctuates in a very small scope during experiments with k fold cross validation method.更多还原