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On the Optimality of Classifier Chain for Multi-label Classification
Date: 2016/5/6             Browse: 233

On the Optimality of Classifier Chain for Multi-label Classification

Speaker: Ivor W Tsang

Time: May 6, 4:50pm - 5:50pm.

Location: Room 306, Teaching Center


To capture the interdependencies between labels in multi-label classification problems, classifier chain (CC) tries to take the multiple labels of each instance into account under a deterministic high-order Markov Chain model. Since its performance is sensitive to the choice of label order, the key issue is how to determine the optimal label order for CC. In this work, we first generalize the CC model over a random label order. Then, we present a theoretical analysis of the generalization error for the proposed generalized model. Based on our results, we propose a classifier chain-dynamic programming (CC-DP) algorithm to search the globally optimal label order for CC and a classifier chain-greedy (CC-Greedy) algorithm to find a locally optimal CC. Comprehensive experiments on a number of real-world multi-label data sets from various domains demonstrate that our proposed CC-DP algorithm outperforms state-of-the-art approaches and the CC-Greedy algorithm achieves comparable prediction performance with CC-DP.


Ivor W Tsang is a Future Fellow and Associate Professor with the Centre for Quantum Computation & Intelligent Systems (QCIS), at the University of Technology Sydney.  His research focuses on transfer learning, feature selection, big data analytics for data with trillions of dimensions, and their applications to computer vision and pattern recognition. He has more than 100 research papers published in top-tier journal and conference papers, including 4 JMLR, 10 TPAMI, 19 TNN/TNNLS and 13 ICML, etc. His H-index in Google Scholar is 40.

In 2009, Dr Tsang was conferred the 2008 Natural Science Award (Class II) by Ministry of Education, China, which recognized his contributions to kernel methods. In 2013, Dr Tsang received his prestigious Australian Research Council Future Fellowship for his research regarding Machine Learning on Big Data. In addition, he had received the prestigious IEEE Transactions on Neural Networks Outstanding 2004 Paper Award in 2007, the 2014 IEEE Transactions on Multimedia Prize Paper Award, and a number of best paper awards and honors from reputable international conferences, including the Best Student Paper Award at CVPR 2010, and the Best Paper Award at ICTAI 2011. He was also awarded the ECCV 2012 Outstanding Reviewer Award. He also served as an Area Chair for NIPS, IJCAI and AAAI.


SIST-Seminar 16031