CAJ | 학술논문

由于多类不平衡数据中某些类别的样例数特别少，使得基于支持度-置信度的关联分类方法在这些类上产生的规则较少，甚至没有，从而导致这些类别的样例很难准确分类。针对此问题，文中提出改进的多类不平衡数据关联分类算法。为了提取更多小类的规则，根据项集与类别的正相关度提取规则。为了提高小类规则的优先级，提出利用项集类分布规则强度排序规则。此外，为解决规则冲突或无规则匹配问题，结合 KNN 分类新样例。实验表明，与基于支持度-置信度的关联分类方法相比，文中算法能提取更多的小类规则，且提高小类规则的优先级，在多类不平衡数据上取得较高的 G-mean 值和 F-score 值。
유우다류불평형수거중모사유별적양례수특별소，사득기우지지도-치신도적관련분류방법재저사류상산생적규칙교소，심지몰유，종이도치저사유별적양례흔난준학분류。침대차문제，문중제출개진적다류불평형수거관련분류산법。위료제취경다소류적규칙，근거항집여유별적정상관도제취규칙。위료제고소류규칙적우선급，제출이용항집류분포규칙강도배서규칙。차외，위해결규칙충돌혹무규칙필배문제，결합 KNN 분류신양례。실험표명，여기우지지도-치신도적관련분류방법상비，문중산법능제취경다적소류규칙，차제고소류규칙적우선급，재다류불평형수거상취득교고적 G-mean 치화 F-score 치。
Instances in some classes are rare in multiclass imbalanced datasets and therefore few rules for these classes are generated by support-confidence based associative classification algorithms. Consequently, instances in these minority classes are difficult to be correctly classified. Aiming at this problem, an improved associative classification algorithm for multiclass imbalanced datasets is proposed. To extract more rules for minority classes, rules are extracted according to positive correlation between itemsets and classes. Then, to improve the priority of minority classes rules, the rule strength based on itemsets class distribution is designed to rank rules. Finally, to address problems of no matched rules or matched rules in conflict, a k nearest neighbor algorithm is incorporated into the improved associative classification to classify new instances. Experimental results show that the proposed algorithm extracts more minority classes rules and promotes the priority of the minority classes rules compared with support-confidence based associative classification, and thus G-mean and F-score value for multiclass imbalance datasets are improved.