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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Multi-label classification by polytree-augmented classifier chains with label-dependent features
| Title: | Multi-label classification by polytree-augmented classifier chains with label-dependent features |
| Authors: | Sun, Lu Browse this author | | Kudo, Mineichi Browse this author →KAKEN DB |
| Keywords: | Multi-label classification | | Label correlation | | Polytree-augmented classifier chain | | Label-dependent feature | | Label imbalance |
| Issue Date: | Aug-2019 |
| Publisher: | Springer |
| Journal Title: | Pattern analysis and applications |
| Volume: | 22 |
| Issue: | 3 |
| Start Page: | 1029 |
| End Page: | 1049 |
| Publisher DOI: | 10.1007/s10044-018-0711-6 |
| Abstract: | Multi-label classification faces several critical challenges, including modeling label correlations, mitigating label imbalance, removing irrelevant and redundant features, and reducing the complexity for large-scale problems. To address these issues, in this paper, we propose a novel methodpolytree-augmented classifier chains with label-dependent featuresthat models label correlations through flexible polytree structures based on low-dimensional label-dependent feature spaces learned by a two-stage feature selection approach. First, a feature weighting approach is applied to efficiently remove irrelevant features for each label and mitigate the effect of label imbalance. Second, a polytree structure is built in the label space using estimated conditional mutual information. Third, an appropriate label-dependent feature subset is found by taking account of label correlations in the polytree. Extensive empirical studies on six synthetic datasets and 12 real-world datasets demonstrate the superior performance of the proposed method. In addition, by incorporating the proposed two-stage feature selection approach, the multi-label classifiers with label-dependent features achieve on average 9.4% performance improvement in Exact-Match compared with the original classifiers. |
| Rights: | The final publication is available at link.springer.com |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/79009 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 工藤 峰一
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