Multi-label imbalanced data enrichment process in neural net classifier training
Gorn Tepvorachai, C. Papachristou · 2008
Semantic scene classification, robotic state recognition, and many other real-world applications involve multi-label classification with imbalanced data. In this paper, we address these problems by using an enrichment process in neural net training. The enrichment process can manage the imbalanced data and train the neural net with high classification accuracy. Experimental results on a robotic arm controller show that our method has better generalization performance than traditional neural net training in solving the multi-label and imbalanced data problems.