ADAPTIVE ZONING DESIGN BY SUPERVISED LEARNING USING MULTI-OBJECTIVE OPTIMIZATION

Abstract

Zoning is a widespread feature extraction technique for handwritten digit recognition, since it is able to handle handwritten pattern variability. Static techniques for zoning design have recently been superseded by adaptive techniques, in which zoning design is considered as the result of an optimization procedure. This paper presents a new learning strategy to optimal zoning design using multi-objective genetic algorithm. More precisely, the nondominant sorting genetic algorithm II (NSGA II) has been applied to define, in a single process, both the optimal number of zones and the optimal zones for the Voronoi-based zoning method. The experimental tests, carried out in the field of handwritten digit recognition, show the effectiveness of this new approach with respect to traditional dynamic approaches for zoning design, based on single-objective optimization techniques.