ONLINE LEARNING - UNIVERSAL CLASSIFIER

Research Aim

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 project, we aim to develop an online universal classifier capable of performing all the three types of aforementioned classification problems.

Datasets

Binary: Diabetes, Ionosphere

Multi-class: Iris, Waveform, Balance-scale

Multi-label: Scene, Yeast, Corel5k, Medical

Dataset specifications are given below:

Algorithm

Code

The source code for online learning of universal classifier will be uploaded in github soon.

Results

More detailed discussions and results are available in the paper:

An Online Universal Classifier for Binary, Multi-class and Multi-label Classification [PDF]