MLPSVM:A new parallel support vector machine to multi-label learning
This addresses multi-label classification tasks where existing methods like Binary Relevance ignore label correlations, though it appears incremental in approach.
The paper tackles the problem of multi-label learning by proposing MLPSVM, a new algorithm that modifies standard support vector machines to use two parallel decision hyperplanes instead of a single one. Experimental results show it performs well compared to other multi-label learning algorithms on datasets.
Multi-label learning has attracted the attention of the machine learning community. The problem conversion method Binary Relevance converts a familiar single label into a multi-label algorithm. The binary relevance method is widely used because of its simple structure and efficient algorithm. But binary relevance does not consider the links between labels, making it cumbersome to handle some tasks. This paper proposes a multi-label learning algorithm that can also be used for single-label classification. It is based on standard support vector machines and changes the original single decision hyperplane into two parallel decision hyper-planes, which call multi-label parallel support vector machine (MLPSVM). At the end of the article, MLPSVM is compared with other multi-label learning algorithms. The experimental results show that the algorithm performs well on data sets.