A TCART-M - Tuned CARTesian-based error function for multilabel classification with the MLP

Jacek Mandziuk, Adam Zychowski, Lipo Wang

Abstract: In 2006 Zhang and Zhou proposed a multilabel classification model based on the MLP network, which was subsequently improved by Grodzicki et al. This paper further improves both these approaches by introducing a scaling parameter responsible for maintaining a balance between the impacts of particular components of the MLP's error function in the training process. The newly-proposed parameter is autonomously fine-tuned by the system in the nested cross validation process. The proposed approach is tested on a set of well-established benchmarks and demonstrates its superiority over the baseline methods for 16 different error measures used in the experiments. Furthermore, the method proves competitive to 12 other state-of-the-art machine learning approaches which are used for further comparisons. In the combined score composed of ranking positions for all benchmarks and all error functions, the proposed neural network system gains the leading position among all tested methods.