Distinct Chains for Different Instances: An Effective Strategy for Multi-label Classifier Chains

• Published in: Machine Learning and Knowledge Discovery in Databases Vol. 8725; pp. 453 - 468
• Main Authors: da Silva, Pablo Nascimento, Gonçalves, Eduardo Corrêa, Plastino, Alexandre, Freitas, Alex A.
• Format: Book Chapter
• Language: English
• Published: Germany Springer Berlin / Heidelberg 2014; Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: Artificial intelligence; Classification; Classifier Chains; Data mining; Multi-Label Classification; Pattern recognition
• ISBN: 9783662448502; 3662448505
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-662-44851-9_29

Multi-label classification (MLC) is a predictive problem in which an object may be associated with multiple labels. One of the most prominent MLC methods is the classifier chains (CC). This method induces q binary classifiers, where q represents the number of labels. Each one is responsible for predicting a specific label. These q classifiers are linked in a chain, such that at classification time each classifier considers the labels predicted by the previous ones as additional information. Although the performance of CC is largely influenced by the chain ordering, the original method uses a random ordering. To cope with this problem, in this paper we propose a novel method which is capable of finding a specific and more effective chain for each new instance to be classified. Experiments have shown that the proposed method obtained, overall, higher predictive accuracies than the well-established binary relevance, CC and CC ensemble methods.