Non-parametric predictive inference for solving multi-label classification

Abstract

Decision Trees (DTs) have been adapted to Multi-Label Classification (MLC). These adaptations are known as Multi-Label Decision Trees (ML-DT). In this research, a new ML-DT based on the Nonparametric Predictive Inference Model on Multinomial data (NPI-M) is proposed. The NPI-M is an imprecise probabilities model that provides good results when it is applied to DTs in standard classification. Unlike other models based on imprecise probabilities, the NPI-M is a nonparametric approach and it does not make unjustified assumptions before observing data. It is shown that the new ML-DT based on the NPI-M is more robust to noise than the ML-DT based on precise probabilities. As the intrinsic noise in MLC might be higher than in traditional classification, it is expected that the new ML-DT based on the NPI-M outperforms the already existing ML-DT. This fact is validated with an exhaustive experimentation carried out in this work on different MLC datasets with several levels of added noise. In it, many MLC evaluation metrics are employed in order to measure the performance of the algorithms. The experimental analysis shows that the proposed ML-DT based on NPI-M obtains better results than the ML-DT that uses precise probabilities, especially when we work on data with noise.