Multi-Label Classification Algorithm for Adaptive Heterogeneous Classifier Group - Meng Han; Shurong Yang; Hongxin Wu; Jian Ding (Mathematics)
Ensemble classification is widely used in multi-label algorithms, and it can be divided into homogeneous ensembles and heterogeneous ensembles according to classifier types. A heterogeneous ensemble can generate classifiers with better diversity than a homogeneous ensemble and improve the performance of classification results. An Adaptive Heterogeneous Classifier Group (AHCG) algorithm is proposed. The AHCG first proposes the concept of a Heterogeneous Classifier Group (HCG); that is, two groups of different ensemble classifiers are used in the testing and training phases. Secondly, the Adaptive Selection Strategy (ASS) is proposed, which can select the ensemble classifiers to be used in the test phase. The least squares method is used to calculate the weights of the base classifiers for the in-group classifiers and dynamically update the base classifiers according to the weights. A large number of experiments on seven datasets show that this algorithm has better performance than most existing ensemble classification algorithms in terms of its accuracy, example-based F1 value, micro-averaged F1 value, and macro-averaged F1 value.