Multi-Label Classification with Meta-Label-Specific Features and Q-Learning

Classification is a crucial process in data mining‎, ‎data science‎, ‎machine learning‎, ‎and the applications of natural language processing‎. ‎Classification methods distinguish the correlation between the data and the output classes‎. ‎In single-label classification (SLC)‎, ‎each input sample is associated with only one class label‎. ‎In certain real-world applications‎, ‎data instances may be assigned to more than one class‎. ‎The type of classification which is required in such applications is known as multi-label classification (MLC)‎. ‎In MLC‎, ‎each sample of data is associated with a set of labels‎. ‎Due to the presence of multiple class labels‎, ‎the SLC learning process is not applicable to MLC tasks‎. ‎Many solutions to the multi-label classification problem have been proposed‎, ‎including BR‎, ‎FS-DR‎, ‎and LLSF‎. ‎But‎, ‎these methods are not as accurate as they could be‎. ‎In this paper‎, ‎a new multi-label classification method is proposed based on graph representation‎. ‎A feature selection technique and the Q-learning method are employed to increase the accuracy of the proposed algorithm‎. ‎The proposed multi-label classification algorithm is applied to various standard multi-label datasets‎. ‎The results are compared with state-of-the-art algorithms based on the well-known performance evaluation metrics‎. ‎Experimental results demonstrated the effectiveness of the proposed model and its superiority over the other methods.