Soft decision trees: a new approach using non-linear fuzzification

This paper investigates the fuzzification of crisp decision trees using nonlinear membership functions to soften sharp decision boundaries. A novel nonlinear fuzzy algorithm provides the framework for the investigation of four different membership functions. Using a genetic algorithm (GA), various sized fuzzy regions are optimised from a training set and are applied to all decision nodes. A new case passing through the tree will result in a membership grade being generated at each branch. Three different fuzzy inference mechanisms, also optimised by the GA, are used to investigate the degree of interaction between membership grades on each specific decision path. Initial comparisons between crisp trees and the fuzzified trees show that the fuzzy tree is more robust and produces a more balanced classification leading to improved decision-making.