Name
Abstract | ||
|---|---|---|
It has recently been suggested that assuming independence between labels is not suitable for real-world multi-label classification. To account for label dependencies, this paper proposes a supervised topic modeling algorithm, namely labelset topic model (LsTM). Our algorithm uses two labelset layers to capture label dependencies. LsTM offers two major advantages over existing supervised topic modeling algorithms: it is straightforward to interpret and it allows words to be assigned to combinations of labels, rather than a single label. We have performed extensive experiments on several well-known multi-label datasets. Experimental results indicate that the proposed model achieves performance on par with and often exceeding that of state-of-the-art methods both qualitatively and quantitatively. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s10844-014-0352-1 | Journal of Intelligent Information Systems |
Keywords | Field | DocType |
Multi-label classification,Topic model,Labelset,Label dependency | Document classification,Data mining,Computer science,Multi-label classification,Artificial intelligence,Topic model,Machine learning | Journal |
Volume | Issue | ISSN |
46 | 1 | 0925-9902 |
Citations | PageRank | References |
1 | 0.35 | 26 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ximing Li | 1 | 44 | 13.97 |
| Jihong OuYang | 2 | 94 | 15.66 |
| Xiaotang Zhou | 3 | 19 | 4.08 |