Name
Abstract | ||
|---|---|---|
Label powerset (LP) method is one category of multi-label learning algorithm. This paper presents a basis expansions model for multi-label classification, where a basis function is an LP classifier trained on a random k-labelset. The expansion coefficients are learned to minimize the global error between the prediction and the ground truth. We derive an analytic solution to learn the coefficients efficiently. We further extend this model to handle the cost-sensitive multi-label classification problem, and apply it in social tagging to handle the issue of the noisy training set by treating the tag counts as the misclassification costs. We have conducted experiments on several benchmark datasets and compared our method with other state-of-the-art multi-label learning methods. Experimental results on both multi-label classification and cost-sensitive social tagging demonstrate that our method has better performance than other methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TKDE.2013.112 | IEEE Trans. Knowl. Data Eng. |
Keywords | Field | DocType |
multi-label classification,ensemble method,cost-sensitive learning,random processes,labelset,learning (artificial intelligence),lp classifier,pattern classification,tag counts,social tag,noisy training set handling,global error minimization,generalized k-labelsets ensemble,tag count,social tagging,multilabel learning algorithm,cost sensitive multilabel classification problem,random k-labelset,expansion coefficients,misclassification cost,social networking (online),minimisation,hypergraph,label powerset method,optimization,learning artificial intelligence,linear programming,prediction algorithms | Data mining,Computer science,Hypergraph,Multi-label classification,Minimisation (psychology),Linear programming,Artificial intelligence,Basis function,Classifier (linguistics),Pattern recognition,Stochastic process,Ground truth,Machine learning | Journal |
Volume | Issue | ISSN |
26 | 7 | 1041-4347 |
Citations | PageRank | References |
18 | 0.56 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hung-Yi Lo | 1 | 118 | 8.33 |
| Shou-De Lin | 2 | 706 | 84.81 |
| Hsin-min Wang | 3 | 1201 | 129.62 |