An Online Universal Classifier for Binary, Multi-class and Multi-label Classification
This addresses the problem of needing separate classifiers for different classification types, offering a unified solution for streaming data applications, though it appears incremental in combining existing classification approaches.
The paper tackles the lack of a single classifier that can handle binary, multi-class, and multi-label classification by proposing a novel online universal classifier, achieving results comparable to state-of-the-art techniques across all three types.
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.