Multi-Label Learning through Boolean Matrix Factorization and Neural Networks
In multi-label learning,utilizing label correlation to improve prediction performance is a research fo-cus.Mining label correlation is an important content.The method based on matrix decomposition obtains a more essential mapping from data to label space by constructing potential labels.However,real number matrix decom-position lacks semantic interpretation and the commonly used linear mapping fitting ability is limited.Therefore,this article proposes a multi-label learning method MLBF based on Boolean matrix factorization and neural net-works.Specifically,the Boolean matrix maintains the semantic features of labels regarding presence/absence,and the proposed heuristic decomposition algorithm is efficient and effective;neural networks provide nonlinear fitting capabilities and effectively utilize parallel computing resources to cope with large datasets.This study conducted experiments on 13 benchmark datasets,compared 8 popular algorithms,and evaluated them using 5 commonly used indicators.The experimental results show that the mean ranking of MLBF in these indicators is 1.92,2.5,2.38,2.23,and 2.46,respectively.
NETL
NSTL
万方数据
维普
