Agge: A novel method to automatically generate rule induction classifiers using grammatical evolution

Abstract

One of the main and fundamental tasks of data mining is the automatic induction of classification rules from a set of examples and observations. A variety of methods performing this task have been proposed in the recent literature. Many comparative studies have been carried out in this field. However, the main common feature between thesemethods is that they are designedmanually. In the meanwhile, there have been some successful attempts to automatically design such methods using Grammar-based Genetic Programming (GGP). In this paper, we propose a different system called Automatic Grammar Genetic Programming (AGGP) that can evolve complete java program codes. These codes represent a rule induction algorithm that uses a grammar evolution technique that governs a Backus Naur Form grammar definition mapping to a program. To perform this task, we will use binary strings as inputs to the mapper along with the Backus Naur Form grammar. Such binary strings represent possible potential solutions resulting from the initialized component and Weka building blocks, this would ease the induction process and makes induced programs short. Experimental results prove the efficiency of the proposed method. It is also shown that, compared to some recent and similar manual techniques (Prism, Ripper, Ridor, OneRule) the proposed method outperforms such techniques.Abenchmark of well-known data sets is used for the sake of comparison.