A simple data discretizer
This is an incremental improvement for researchers and practitioners in machine learning who need efficient discretization methods to enhance classifier performance.
The paper tackles the problem of data discretization for machine learning by introducing a modified version of the Minimum Information Loss (MIL) algorithm, which improves classification accuracy in several cases compared to MIL and Minimum Description Length Principle (MDLP).
Data discretization is an important step in the process of machine learning, since it is easier for classifiers to deal with discrete attributes rather than continuous attributes. Over the years, several methods of performing discretization such as Boolean Reasoning, Equal Frequency Binning, Entropy have been proposed, explored, and implemented. In this article, a simple supervised discretization approach is introduced. The prime goal of MIL is to maximize classification accuracy of classifier, minimizing loss of information while discretization of continuous attributes. The performance of the suggested approach is compared with the supervised discretization algorithm Minimum Information Loss (MIL), using the state-of-the-art rule inductive algorithms- J48 (Java implementation of C4.5 classifier). The presented approach is, indeed, the modified version of MIL. The empirical results show that the modified approach performs better in several cases in comparison to the original MIL algorithm and Minimum Description Length Principle (MDLP) .