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

arXiv: https://arxiv.org/abs/1609.00843 · HTML: https://mlsanity.com/paper/1609.00843/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2016.

## Built on by (most cited first, 2 of 2)

- [Deep Sequence Models for Text Classification Tasks](https://mlsanity.com/paper/2207.08880/) (2022)
- [A Systematic Literature Review on Multi-label Data Stream Classification](https://mlsanity.com/paper/2508.17455/) (2025)

## Abstract (arXiv)

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.