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Multi label classification is a variation of single label classification problem where each instance is associated with more than one class label. The foremost unremarkably used approach to handle multi-label classification problem is to transfer multi-label problem into single label problems, where binary classifier is learned independently for every attainable class labels. However, multi-labeled data generally exhibit relationships between labels, but multi-label classification approach fails to take such relationships under consideration. It's understood that in this type of classification, labels co-relationship should be maintain. Label co-relationships can be visualized either in tree structure hierarchies or in DAG (Directed Acyclic Graph) structure hierarchies. These hierarchical arrangement of labels maintain the hierarchical constraint that is once an instance belongs to some class that automatically belongs to all its super classes. This book presents several variations to the induction of decision tree using Predictive Clustering Tree (PCT) algorithm for Hierarchical Multi-label Classification.
About the author
Purvi Prajapati has received her Bachelor Degree in Information Technology from Sardar Patel University, Vidhyanagar, Gujarat, India in 2004 and Master Degree in Computer Engineering from Charusat University, Gujarat, India in 2012. Her current research interest includes multi-label classification in Data Mining.
