Performance improvement of extreme multi-label classification using K-way tree construction with parallel clustering algorithm

Abstract

Abstract eXtreme Multi-Label Classification (XMLC) is the particular case of Multi-Label Classification, which deals with an extremely high number of labels. The main goal is to learn extreme classifier which extracts the subset of relevant labels from extremely large label space. In an extreme environment, one of the big issues is to deal with an extreme number of features, labels and instances which affects the performance of the classifier. The high dimensional feature space and label space makes existing approaches intractable in terms of data scalability, data sparsity, training and prediction cost. The appropriate input representation technique can be used to maintain the interdependency among labels and correlation between feature space and label space. The proposed approach called “K-way Tree based eXtreme Multi-Label Classifier (KTXMLC)“ works on tree based classifier to maintain correlations using feature-label input representation technique and node partitioning using a clustering algorithm. KTXMLC constructs multi-way multiple trees using a parallel clustering algorithm, which leads to fast computational cost. KTXMLC outperforms over the existing tree based classifier in terms of ranking based measures on six datasets named Delicious, Mediamill, Eurlex-4K, Wiki10-31K, AmazonCat-13K, Delicious-200K.