Evolution of Genetic Algorithms in Classification Rule Mining

Evolution of Genetic Algorithms in Classification Rule Mining

Dipankar Dutta (University Institute of Technology, The University of Burdwan, India) and Jaya Sil (Bengal Engineering and Science University, India)
DOI: 10.4018/978-1-4666-2518-1.ch013

Abstract

Classification is one of the most studied areas of data mining, which gives classification rules during training or learning. Classification rule mining, an important data-mining task, extracts significant rules for classification of objects. In this chapter class specific rules are represented in IF THEN form. With the popularity of soft computing methods, researchers explore different soft computing tools for rule discovery. Genetic algorithm (GA) is one of such tools. Over time, new techniques of GA for forming classification rules are invented. In this chapter, the authors focus on an understanding of the evolution of GA in classification rule mining to get an optimal rule set that builds an efficient classifier.
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Literature Review

The classification techniques and specifically rule discovery to build the classifiers are broadly categorized as soft computing based methods and non-soft computing based methods.

Key Terms in this Chapter

Pareto Optimal Font: It is a font on which a set of dominated Pareto optimal solutions lays.

Mutation: It is process of GA, which changes two or more gene values in a chromosome from its initial state to add diversity in the population.

Feature Selection: It is a technique of selecting a subset of features from data sets for building classifier.

Data Discritization: It is a process of converting continuous feature values into a finite set of intervals.

Crossover: It is process of GA in which two or more parent chromosomes exchanges information to produce two or more child chromosomes to gain diversity in the population.

Conflicting Objectives: It is a set of desired objectives that negatively influence each other.

MOGA: It is type of GA, which can deal with multiple conflicting objectives to find out optimized solutions by preserving population diversity.

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