Imprecise Deep Forest for Partial Label Learning

In partial label (PL) learning, each instance corresponds to a set of candidate labels, among which only one is valid. The objective of PL learning is to obtain a multi-class classifier from the training instances. Because the true label of a PL training instance is hidden in the candidate label set and inaccessible to the learning algorithm, the training process of the classifier is significantly challenging. This study proposes a novel deep learning method for PL learning based on the improved error-correcting output codes (ECOC) algorithm and deep forest (DF) framework. For the ECOC algorithm, we extract the prior knowledge of the candidate label sets from the PL training set to optimize the generation of its coding matrix, where different binary training sets can be derived from the PL training set based on the dichotomy corresponding to each column code. For the DF framework, this improved ECOC algorithm is embedded as a unit in its cascade structure; moreover, an imprecise evaluation method is designed to determine the growth of the cascade of the DF. The effectiveness of the proposed method is verified by conducting several experiments on artificial and real-world PL datasets.