Optical music recognition as the case of imbalanced pattern recognition: A study of complex classifiers

Abstract

The article is focused on a particular aspect of classification, namely the imbalance of recognized classes. Imbalanced data adversely affects the recognition ability and requires proper classifier’s construction. The aim of presented study is to explore the capabilities of classifier combining methods with such raised problem. In this paper authors discuss results of experiment of imbalanced data recognition on the case study of music notation symbols. Applied classification methods include: simple voting method, bagging and random forest. Keywords: music recognition, ensemble classifiers, imbalanced data.