A genetic-based subspace analysis method for improving Error-Correcting Output Coding

Two key factors affecting the performance of Error Correcting Output Codes (ECOC) in multiclass classification problems are the independence of binary classifiers and the problem-dependent coding design. In this paper, we propose an evolutionary algorithm-based approach to the design of an application-dependent codematrix in the ECOC framework. The central idea of this work is to design a three-dimensional codematrix, where the third dimension is the feature space of the problem domain. In order to do that, we consider the feature space in the design process of the codematrix with the aim of improving the independence and accuracy of binary classifiers. The proposed method takes advantage of some basic concepts of ensemble classification, such as diversity of classifiers, and also benefits from the evolutionary approach for optimizing the three-dimensional codematrix, taking into account the problem domain. We provide a set of experimental results using a set of benchmark datasets from the UCI Machine Learning Repository, as well as two real multiclass Computer Vision problems. Both sets of experiments are conducted using two different base learners: Neural Networks and Decision Trees. The results show that the proposed method increases the classification accuracy in comparison with the state-of-the-art ECOC coding techniques.

• We improve the efficiency of ECOC by using different feature sets for each dichotomizer.
• Three-dimensional codematrix is generated, where the new dimension is the feature space.
• GA is employed to optimize the 3D codematrix taking into account the problem at hand.
• Consequently, more independent and accurate problem-dependent classifiers are built.
• Classification accuracy improves in comparison with the state-of-the-art methods.