An Automated Unsupervised Discretization Method: A Novel Approach

Abstract

In this paper, for the first time, a novel discretization scheme is proposed aiming at enabling scalability but also at least three other strong challenges. It is based on a Left-to-Right (LR) scanning process, which partitions the input stream into intervals. This task can be implemented by an algorithm or by using a generator that builds automatically the discretization program. We focus especially on unsupervised discretization and design a method called Usupervised Left to Right Discretization (ULR-Discr). Extensive experiments were conducted using various cut-point functions on small, large and medical public datasets. First, ULR-Discr variants under different statistics are compared between themselves with the aim at observing the impact of the cut-point functions on accuracy and runtime. Then the proposed method is compared to traditional and recent techniques for classification. The result is that the classification accuracy is highly improved when using our method for discretization.