Genetic fuzzy discretization with adaptive intervals for classification problems

We propose a genetic fuzzy discretization method for continuous numerical attributes. Traditional discretization methods categorize the continuous attributes into a number of bins. Because they are made on crisp discretization, there exists considerable information loss. Fuzzy discretization allows overlapping intervals and reflects linguistic classification. However, the number of intervals, the boundaries of intervals, and the degrees of overlapping are intractable to get optimized. We use a genetic algorithm to optimize these parameters. Experimental results showed considerable improvement on the classification accuracy over a crisp discretization and a typical fuzzy discretization.