Probabilistic Classifier Chain Inference via Gibbs Sampling

Multi-label classification is supervised learning, where an instance may be assigned with multiple categories (labels) simultaneously. Recently, a method called Probabilistic Classifier Chain (PCC) was proposed with numerous appealing properties, such as conceptual simplicity, flexibility, and theoretical justification. Nevertheless, PCC suffers from high inference complexity. To address this problem, we propose a novel inference method with gibbs sampling. An acceleration scheme is proposed to accelerate this method further. Our proposed method is based on our claim that PCC is a special case of Bayesian network. This claim may inspire more inference algorithms for PCC. Experiments with real-world data sets show effectiveness of our proposed method. Copyright 2014 ACM.EI1855-185