A Novel Approach to Classify Imbalanced Dataset Based on Rare Attributes and Double Confidences

2009 Second International Workshop on Knowledge Discovery and Data Mining Pub Date : 2009-01-23 DOI:10.1109/WKDD.2009.20

Yingjie Li, Yixin Yin

引用次数: 1

Abstract

The major weakness of associative classification is examined. A novel approach for classifying imbalanced dataset is proposed. It is an associative classification. Rules which are un-frequent are used to build the classifier rule set. Besides the confidence of pattern “X→Y”, the confidence of pattern “Y→X” is used in the approach. Further more, only features of rare classes are preserved while training. The good performance of the approach is shown by the experiments.

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基于稀有属性和双置信度的不平衡数据分类新方法

分析了联想分类的主要缺点。提出了一种新的不平衡数据分类方法。这是一种关联分类。不频繁的规则用于构建分类器规则集。该方法除了使用模式“X→Y”的置信度外，还使用模式“Y→X”的置信度。而且，训练时只保留稀有类的特征。实验结果表明，该方法具有良好的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2009 Second International Workshop on Knowledge Discovery and Data Mining

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