A PRELIMINARY STUDY ON SELECTING THE OPTIMAL CUT POINTS IN DISCRETIZATION BY EVOLUTIONARY ALGORITHMS
2012
The Discretization, as a data preprocessing technique, has played an important role in many areas such as artificial intelligence, data mining and machine learning. In this paper, we propose the use of evolutionary algorithms to select a subset of cut points that defines the best possible discretization scheme of a data set. First, we identify the boundary points for each input attribute and then we establish the individual representation as the joining of all of them, forming bit-strings based chromosomes. In addition, we consider an inconsistency based fitness function for measuring the quality of the chromosomes during the evolutionary cycle. The CHC model is adopted as evolutionary approach, showing that it can bring higher accuracy to the discretization process. The proposal has been compared with other state-of-the-art and recent discretizers on 20 real data sets and the experiments show that our proposed algorithm generates competitive discretization schemes in terms of accuracy, for both C4.5 and Naive Bayes classifiers, but using a lower number of cut points.