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Data Mining Classification: The Potential of Genetic Programming

Authors:
Nabil El Kadhi
Fatima Habib

Keywords: Data Mining; Genetic Algorithm; Genetic Programming; Classification

Abstract:
Data Mining (DM) is one of the techniques used for the process of searching through a huge volume of data in a database to uncover useful and interesting information. Classification is a commonly studied issue in data mining. It involves predicting the categorical attribute (class) value on the basis of other attributes (predicting attributes) values. One of the classification approaches to data mining is gene expression programming (GEP), which is a development of Genetic algorithm (GA) and Genetic Programming (GP). In this paper, we investigate the potential of genetic programming for data mining classification. It is therefore important to investigate the advantages and challenges associated with using tree-based Genetic Programming algorithms for data mining classification. This study determines how better data mining classification performance can be achieved using genetic programming. It demonstrates how the search scope can be refined through heuristics and machine learning methods to reduce and change the search space for our Genetic Programming classifiers. A specific design and application of GP to two classes data analysis is presented as well as a set of experimental results showing the efficiency of the suggested application.

Pages: 1 to 7

Copyright: Copyright (c) IARIA, 2011

Publication date: June 19, 2011

Published in: conference

ISSN: 2308-4529

ISBN: 978-1-61208-139-7

Location: Luxembourg City, Luxembourg

Dates: from June 19, 2011 to June 24, 2011