Cyclic Classifier Chain for Cost-Sensitive Multilabel Classification

We propose a novel method, Cyclic Classifier Chain (CCC), for multilabel classification. CCC extends the classic Classifier Chain (CC) method by cyclically training multiple chains of labels. Three benefits immediately follow the cyclic design. First, CCC resolves the critical issue of label ordering in CC, and therefore reaches more stable performance. Second, CCC matches the task of cost-sensitive multilabel classification, an important problem for satisfying application needs. The cyclic aspect of CCC allows estimating all labels during training, and such estimates makes it possible to embed the cost information into weights of labels. Experimental results justify that cost-sensitive CCC can be superior to state-of-the-art cost-sensitive multilabel classification methods. Third, CCC can be easily coupled with gradient boosting to inherit the advantages of ensemble learning. In particular, gradient boosted CCC efficiently reaches promising performance for both linear and non-linear base learners. The three benefits, stability, cost-sensitivity and efficiency make CCC a competitive method for real-world applications.