Genetic Programming for Automatically Constructing Data Mining Algorithms

Genetic Programming for Automatically Constructing Data Mining Algorithms

Alex A. Freitas (University of Kent, UK) and Gisele L. Pappa (Federal University of Minas Geras, Brazil)
Copyright: © 2009 |Pages: 5
DOI: 10.4018/978-1-60566-010-3.ch144
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Abstract

At present there is a wide range of data mining algorithms available to researchers and practitioners (Witten & Frank, 2005; Tan et al., 2006). Despite the great diversity of these algorithms, virtually all of them share one feature: they have been manually designed. As a result, current data mining algorithms in general incorporate human biases and preconceptions in their designs. This article proposes an alternative approach to the design of data mining algorithms, namely the automatic creation of data mining algorithms by means of Genetic Programming (GP) (Pappa & Freitas, 2006). In essence, GP is a type of Evolutionary Algorithm – i.e., a search algorithm inspired by the Darwinian process of natural selection – that evolves computer programs or executable structures. This approach opens new avenues for research, providing the means to design novel data mining algorithms that are less limited by human biases and preconceptions, and so offer the potential to discover new kinds of patterns (or knowledge) to the user. It also offers an interesting opportunity for the automatic creation of data mining algorithms tailored to the data being mined.
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Background

An Evolutionary Algorithm (EA) is a computational problem-solving method inspired by the process of natural selection. In essence, an EA maintains a population of “individuals”, where each individual represents a candidate solution to the target problem. EAs are iterative generate-and-test procedures, where at each “generation” (iteration) a population of individuals is generated and each individual has its “fitness” computed. The fitness of an individual is a measure of the quality of its corresponding candidate solution. The higher the fitness of an individual, the higher the probability that the individual will be selected to be a “parent” individual. Certain operations (often “crossover” and/or “mutation” operations inspired by their natural counterparts) are applied to the selected parent individuals in order to produce “children” individuals. The important point is that, since the children are in general produced from parents selected based on fitness, the children (new candidate solutions) tend to inherit parts of the good solutions of the previous generation, and the population as a whole gradually evolves to fitter and fitter individuals (better and better solutions to the target problem). For a comprehensive review of EAs in general the reader is referred to (De Jong, 2006; Eiben & Smith, 2003), and a comprehensive review of EAs for data mining can be found in (Freitas, 2002).

The basic principle of EAs – i.e., artificial evolution of candidate solutions, where parent solutions are selected based on their fitness in order to create new children solutions – still holds in Genetic Programming (GP). The main distinguishing feature of GP – by comparison with other types of EA – is that the candidate solutions represented by GP individuals are (or at least should be) computer programs or executable structures. GP has been an active research field for about 15 years (Koza, 1992; Banzhaf et al., 1998; Langdon & Poli, 2002; Koza, 2003), and Koza (2006) reports 36 instances where GP discovered a solution infringing or duplicating some patent, which led Koza to claim that GP is an automated invention machine that routinely produces human-competitive results. However, the creation of a GP system for automatically evolving a full data mining algorithm, as proposed in this article, is a new research topic which is just now starting to be systematically explored, as discussed in the next section.

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