Title :
A Novel Approach to Classify Imbalanced Dataset Based on Rare Attributes and Double Confidences
Author :
Li Yingjie ; Yin Yixin
Author_Institution :
Inf. Eng. Dept., Univ. of Sci. & Technol. Beijing, Beijing
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 ldquoXrarrYrdquo, the confidence of pattern ldquoYrarrXrdquo 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.
Keywords :
data mining; pattern classification; association rule discovery; associative classification; double confidences; imbalanced dataset classification; rare attributes; Association rules; Classification algorithms; Data engineering; Data mining; Forestry; Itemsets; Knowledge engineering; Logic; Sorting; classification; double confidences; imbalanced dataset; rare attributes;
Conference_Titel :
Knowledge Discovery and Data Mining, 2009. WKDD 2009. Second International Workshop on
Conference_Location :
Moscow
Print_ISBN :
978-0-7695-3543-2
DOI :
10.1109/WKDD.2009.20
