Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel framework to deal with data imbalance in class association rule mining. In each class association rule, the right-hand is a target class while the left-hand may contain one or more attributes. This framework is focused on the multiple imbalanced attributes on the left-hand. In the proposed framework, the rules with and without imbalanced attributes are processed in parallel. The rules without imbalanced attributes are mined through standard algorithm while the rules with imbalanced attributes are mined based on new defined measurements. Through simple transformation, these measurements can be in a uniform space so that only a few parameters need to be specified by user. In the case study, the proposed algorithm is applied into social security field. Although some attributes are severely imbalanced, the rules with minority of the imbalanced attributes have been mined efficiently. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-76928-6_100 | Australian Conference on Artificial Intelligence |
Keywords | Field | DocType |
proposed framework,class association rule mining,novel framework,standard algorithm,target class,imbalanced attribute,multiple imbalanced attribute,case study,proposed algorithm,class association rule,association rule,association rule mining | Uniform space,Data mining,Standard algorithms,Computer science,Association rule learning,Artificial intelligence,Data imbalance,Machine learning | Conference |
Volume | ISSN | ISBN |
4830 | 0302-9743 | 3-540-76926-9 |
Citations | PageRank | References |
6 | 0.53 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huaifeng Zhang | 1 | 240 | 18.84 |
| Yanchang Zhao | 2 | 233 | 20.01 |
| Longbing Cao | 3 | 2212 | 185.04 |
| Chengqi Zhang | 4 | 3636 | 274.41 |