Optimal construction of one-against-one classifier based on meta-learning

Author-Highlights
• ODOAO seeks to construct a one-against-one classifier based on meta-learning.
• ODOAO utilizes binary base classifiers from various classification algorithms.
• A meta-classifier effectively combines the outputs from all the base classifiers.
• The effectiveness of ODOAO is demonstrated through experiments.

A commonly used strategy for solving a multi-class classification problem is to decompose the original problem into several binary subproblems. The recently proposed method, diversified one-against-one (DOAO), constructs a one-against-one classifier by selecting the best classifier for each class pair from the set of heterogeneous base classifiers. It was found to yield better classification accuracy than other one-against-one classifiers that are based on individual classification algorithms. This paper presents a novel method, called optimally diversified one-against-one (ODOAO) which is an improvement of DOAO. ODOAO is based on meta-learning, and seeks to construct a multiple classifier system where a meta-classifier effectively combines the outputs from all the heterogeneous base classifiers that are trained using various classification algorithms for every class pair. Experimental results show that ODOAO outperforms DOAO and other one-against-one based methods with statistical significance.