Discovering Classification Rules and Providing Ranking among Labels to Multi-Target Data Using Novel Approach

Learning from multi-label and multi-target data is always more difficult than single-label data. The complexity is incurred by multiple labels assigned to a sample as well as to provide ranking among labels. The learning algorithms used for single-label data are not suitable to learn from multi-label and multi-target data. There are many applications where multi-label and multi-target data are present, for example, image classification, gene classification, document classification and so on. Multi-label classification can be mainly classified into two methods: Problem Transformation (PT) and Algorithm Adaptation. This paper focuses on classification of multi-target data by applying Pruned Set (PS) method which belongs to PT category. The PS method is capable of modelling label correlations directly and reduces over-fitting of data by pruning and sub-sampling label sets. The objective of this paper is to provide classification rules in multi-label form, provide confusion matrix which shows overall accuracy as well as label-wise accuracy for each different value of every class-label and to provide label-wise ranking among class-labels for multi-target data. Experiments are performed on real multi-target datasets and three measures like Hamming loss, exact match and accuracy are compared with different PT methods. Experimental comparison shows that the PS method gives better overall results when classifying multi-target data as compared to standard PT methods.