A new hybrid learning method for fuzzy decision trees

This paper presents a new hybrid learning method for the construction of fuzzy decision trees. The main principle of this approach is to automatically generates a hierarchical organization of the knowledge coupled with local choice of the best feature subspace. To improve the representation, a double level of modeling is used. Firstly a pre-classification level searches fuzzy decision regions to operate a natural discrimination between classes. The second level refines the previous one, doing an intrinsic fuzzy modeling of the classes represented in the fuzzy regions. Moreover, the best feature subspace is determined locally by a genetic algorithm for each partitioning. Finally, to have an understandable and "transparent" representation, the fuzzy decision tree is formalized as a fuzzy inference system which is easily modifiable and can be optimized a posteriori. First experimental results conducted on classical benchmarks and on a handwritten digits database show the capacity of the hybrid learning approach to provide reliable and compact classification system