Probability-based label enhancement for multi-dimensional classification

Abstract

Multi-dimensional classification (MDC) assumes that each instance has multiple heterogeneous class spaces simultaneously, and each class variable describes the semantic information of instances from a specific dimension. Recent studies have proven that encoding heterogeneous class spaces into a special logical-label space and employing the label enhancement technique to learn latent real-number labels (i.e., label distributions) of instances is an effective strategy for MDC. However, the adopted label enhancement methods can result that data whose features are quite different to each other have similar label distributions. To tackle this problem, we propose a novel probability-based label enhancement approach for MDC. Specifically, manifold structures of the feature and label distribution spaces are transformed into two different probability distributions, and we expect them to be close. Subsequently, it makes label distributions of samples whose features have large differences be more differentiated. Moreover, the logical-label mapping and reconstruction terms are designed to preserve the intrinsic information from the logical-label space. Besides, an improved multi-output support vector regression is developed as the prediction model, where we introduce mean squared error to reduce the risk of model underfitting. Experimental results on ten benchmark datasets clearly validate the superiority of our method over state-of-the-art MDC baselines.