2016/09/03 by Meng Joo Er, Er, Meng Joo, Rajasekar Venkatesan +3 · 1 citation
Computer Science · #Machine Learning and ELM #Text and Document Classification Technologies #Face and Expression Recognition
paper · pdf · doi:10.48550/arxiv.1609.00843
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 sub-categories 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.