Classification involves the learning of the mapping function that associates input samples to corresponding target label. There are two major categories of classification problems: Single-label classification and Multi-label classification. Traditional binary and multi-class classifications are subcategories of single-label classification. Several classifiers are developed for binary, multi-class and multi-label classification problems, but there are no classifiers available in the literature capable of performing all three types of classification. In this paper, a novel online universal classifier capable of performing all the three types of classification is proposed. Being a high speed online classifier, the proposed technique can be applied to streaming data applications. The performance of the developed classifier is evaluated using datasets from binary, multi-class and multi-label problems. The results obtained are compared with state-of-the-art techniques from each of the classification types.
A novel online universal classifier capable of performing the multi-classification problem is proposed in REF .
An online universal classifier for binary, multi-class and multi-label classification
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Computer Science Mathematics