Multi-classifier System Configuration Using Genetic Algorithms
Donato Impedovo, Giuseppe Pirlo, Donato Barbuzzi · 2012
Classifier combination is a powerful paradigm to deal with difficult pattern classification problems. As matter of this fact, multi-classifier systems have been widely adopted in many applications for which very high classification performance is necessary. Notwithstanding, multi-classifier system design is still an open problem. In fact, complexity of multi-classifiers systems make the theoretical evaluation of system performance very difficult and, consequently, also the design of a multi-classifier system. This paper presents a new approach for the design of a multi-classifier system. In particular, the problem of feature selection for a multi-classifier system is addressed and a genetic algorithm is proposed for automatic selecting the optimal set of features for each individual classifier of the multi-classifier system. The experimental results, carried out in the field of handwritten digit recognition, demonstrate the effectiveness of the proposed approach.