An Improved Multi-label Classification Ensemble Learning Algorithm

This paper proposes an improved algorithm based on minimizing the weighted error of mistake labels and miss labels in multi-label classification ensemble learning algorithm. The new algorithm aims to avoid local optimum by redefining weak classifiers. This algorithm considers the correlations of labels under the precondition of ensuring the error drops with the number of weak classifiers increasing. This paper proposes two improved approaches; one introduces combinational coefficients when combining weak classifiers, another smooth the weak classifier's output to avoid local optimum. We discuss the basis of these modifications, and verify the effectiveness of these algorithms. The experimental results show that all the improved algorithms are effective, and less prone to over fitting.