Recently, multi-label classification has gained prime importance among the classification problems. The applications of classification problems has increased so rapidly that the need for efficient and accurate classifiers has become a vital requirement in the area of data mining. Multi-label classification problem is distinguished from the single label classification because of the capability to handle multiple labels. In this paper, we put forward a good method to predict the multiple labels of an unlabeled instance by using apriori algorithm. The proposed solution MLC-A is able to find unknown labels for any test instance by checking for the presence of particular set of attributes, along with their discretized values. MLC-A intends to employ Apriori algorithm for multi-label classification purpose, in contrast, to the well known existing methods. Apriori algorithm is utilized to find the relationship between attributes and labels by generating rules. The generated rules will find the occurrence of a label given the existence of various attributes. The experimental results on datasets of yeast, scene and emotions had given excellent accuracy. Using MLC-A the time complexity has reduced to a great extend. The accuracy and efficiency of the proposed method is evaluated by using hamming loss and is compared to the state of art.
S. Athira, Poojitha, K., and Prathibhamol CP, “An efficient solution for multi-label classification problem using apriori algorithm (MLC-A)”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.