In any Multi label classification problem, each instance is associated with multiple class labels. In this paper, we aim to predict the class labels of the test data accurately, using an improved multi label classification approach. This method is based on a framework that comprises an initial clustering phase followed by rule extraction using FP-Growth algorithm in label space. To predict the label of a new test data instance, this technique searches for the nearest cluster, thereby locating k-Nearest Neighbors within the corresponding cluster. The labels for the test instance are estimated by prior probabilities of the already predicted labels. Hence, by doing so, this scheme utilizes the advantages of the hybrid approach of both clustering and association rule mining.The proposed algorithm was tested on standard multi label datasets like yeast and scene. It achieved an overall accuracy of 81% when compared with scene dataset and a 68% in yeast dataset. © Springer International Publishing Switzerland 2016.
Prathibhamol CP and Asha Ashok, “Solving multi label problems with clustering and nearest neighbor by consideration of labels”, 2nd International Symposium on Signal Processing and Intelligent Recognition Systems (SIRS'15), Advances in Intelligent Systems and Computing, vol. 425. Springer , pp. 511-520, 2016.