Name
Abstract | ||
|---|---|---|
Cost-sensitive feature selection is an important research issue in both machine learning and data mining. Most existing cost-sensitive feature selection work deal with the single-label data. However, in real applications, the data usually is multi-label, continuous and incomplete because of the technology or cost limitations during data collection. To alleviate this problem, a cost-sensitive feature selection algorithm is proposed here for incomplete neighborhood multi-label which can implement feature selection based on considering about the weighted test cost. The experimental results show that our algorithm can select a low-cost feature subset without losing the classification accuracy. The effectiveness and feasibility of the proposed algorithm is verified by the performance on the three Mulan datasets. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICMLC.2018.8526938 | 2018 International Conference on Machine Learning and Cybernetics (ICMLC) |
Keywords | Field | DocType |
Cost-sensitive,Feature selection,Incomplete data,Multi-label classification | Data collection,Feature selection,Computer science,Algorithm,Feature extraction,Rough set,Artificial intelligence,Statistical classification,Machine learning | Conference |
Volume | ISSN | ISBN |
1 | 2160-133X | 978-1-5386-5215-2 |
Citations | PageRank | References |
1 | 0.35 | 17 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qin Huang | 1 | 30 | 11.60 |
| Wenbin Qian | 2 | 21 | 1.28 |
| Wenhao Shu | 3 | 123 | 11.98 |
| Binglong Wu | 4 | 1 | 0.35 |
| Shuangshuang Feng | 5 | 1 | 0.35 |