Abstract:

Multi-label learning, combining RBF neural network and K-means clustering algorithm, has achieved good effects. But because the number of clusters cannot be well determined in advance, an accurate value of the clustering cannot be obtained. This problem will lead to lower quality clustering and  clustering instability,  and then affect the stability and the classification performance of the multi-label RBF neural network algorithm. To solve the optimization problems, from the angle of sample geometry, an index function for clustering validity was employed to find the optimal number of clusters for each class. Theoretical research and experimental results show that the improved ML-IRBF algorithm can effectively boost better performance in terms of the stability and capability of classification.

Key words: multi-label learning; RBF neural network; K-means clustering; optimization; index function