A High Speed Multi-label Classifier based on Extreme Learning Machines
This work addresses the need for efficient multi-label classification in domains like multimedia, text, and biology, but it is incremental as it adapts an existing technique to a broader problem.
The authors tackled multi-label classification by extending extreme learning machines to handle multiple labels per sample, achieving faster execution speed and better performance across six benchmark datasets compared to state-of-the-art methods.
In this paper a high speed neural network classifier based on extreme learning machines for multi-label classification problem is proposed and dis-cussed. Multi-label classification is a superset of traditional binary and multi-class classification problems. The proposed work extends the extreme learning machine technique to adapt to the multi-label problems. As opposed to the single-label problem, both the number of labels the sample belongs to, and each of those target labels are to be identified for multi-label classification resulting in in-creased complexity. The proposed high speed multi-label classifier is applied to six benchmark datasets comprising of different application areas such as multi-media, text and biology. The training time and testing time of the classifier are compared with those of the state-of-the-arts methods. Experimental studies show that for all the six datasets, our proposed technique have faster execution speed and better performance, thereby outperforming all the existing multi-label clas-sification methods.