Name
Abstract | ||
|---|---|---|
The hierarchical classification with an imbalance class problem is a challenge for in machine learning, and is caused by data with an uneven distribution. Learning from an imbalanced dataset can lead to performance degradation of the classifier. Cost-sensitive learning is a useful solution for handling the gap probability of majority and minority classes. This paper proposes a cost-sensitive hierarchical classification for imbalance classes (CSHCIC), constructing a cost-sensitive factor to balance the relationship between majority and minority classes. First, we divide a large hierarchical classification task into several small subclassification tasks by class hierarchy. Second, we establish a cost-sensitive factor by more precisely using the number of different samples of subclassifications. Then, we calculate the probability of every node using logistic regression. Lastly, we update the cost-sensitive factor using the flexibility factor and the number of samples. The experimental results show that the cost-sensitive hierarchical classification method achieves excellent performance on handling imbalance class datasets. The running time cost of the proposed method is smaller than most state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s10489-019-01624-z | Applied Intelligence |
Keywords | DocType | Volume |
Imbalance class, Misclassification cost, Cost-sensitive, Hierarchical classification | Journal | 50 |
Issue | ISSN | Citations |
8 | 0924-669X | 1 |
PageRank | References | Authors |
0.35 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weijie Zheng | 1 | 26 | 5.87 |
| Hong Zhao | 2 | 105 | 16.53 |