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

fYear

2009

fDate

23-25 Jan. 2009

Firstpage

535

Lastpage

538

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge Discovery and Data Mining, 2009. WKDD 2009. Second International Workshop on

Conference_Location

Moscow

Print_ISBN

978-0-7695-3543-2

Type

conf

DOI

10.1109/WKDD.2009.20

Filename

4771992

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=483275