An Online Universal Classifier For Binary Multiclass And Multilabel Classification

# An Online Universal Classifier for Binary, Multi-class and Multi-label Classification

**arXiv ID:** 1609.00843
**Authors:** Meng Joo Er, Rajasekar Venkatesan, Ning Wang
**Published:** 2016-09-03T17:03:14Z
**Abstract:**
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.

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## References

- [arXiv:1609.00843](http://arxiv.org/abs/1609.00843v1)