Name
Title | ||
|---|---|---|
An approach for multi-label classification by directed acyclic graph with label correlation maximization. |
Abstract | ||
|---|---|---|
Traditional supervised learning approaches primarily work in the single-label environment. However, in many real-world problems, data instances are usually associated with multiple labels simultaneously, and multi-label learning is increasingly required in many modern applications. In multi-label learning, the key to successful classification is effectively exploiting the complex correlations among the output labels. This paper proposes a novel multi-label learning method inspired by the classifier chain approach. The main contribution of this work is to model the correlations of the labels using a directed acyclic graph. Starting from the simple intuitive notion of measuring the correlations among the labels, the proposed method is designed as a multi-label learning method that maximizes the correlations among labels. To evaluate its effectiveness, the proposed method is compared with the state-of-the-art approaches. Extensive experiments demonstrated the proposed method to be highly competitive with the other multi-label approaches. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.ins.2016.02.037 | Inf. Sci. |
Keywords | Field | DocType |
Multi-label learning,Directed acyclic graph,Bayesian network,Conditional entropy | Stability (learning theory),Pattern recognition,Supervised learning,Multi-label classification,Directed acyclic graph,Bayesian network,Artificial intelligence,Conditional entropy,Classifier (linguistics),Machine learning,Maximization,Mathematics | Journal |
Volume | Issue | ISSN |
351 | C | 0020-0255 |
Citations | PageRank | References |
13 | 0.61 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaedong Lee | 1 | 13 | 1.63 |
| Heera Kim | 2 | 16 | 1.13 |
| Noo-ri Kim | 3 | 27 | 4.55 |
| Jee-Hyong Lee | 4 | 316 | 49.65 |