Evolutionary Approaches for the Extraction of Classification Rules

Evolutionary Approaches for the Extraction of Classification Rules

Sadjia Benkhider (Laboratory of Research on Artificial Intelligence, University of Sciences and Technology Algiers, Algiers, Algeria), Ahmed Riadh Baba-Ali (Laboratory LRPE, University of Sciences and Technology Algiers, Algiers, Algeria) and Habiba Drias (Laboratory of Research on Artificial Intelligence, University of Sciences and Technology Algiers, Algiers, Algeria)
Copyright: © 2014 |Pages: 19
DOI: 10.4018/ijamc.2014010101
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Abstract

This paper provides evolutionary approaches in order to extract comprehensible and accurate classification rules. Indeed to construct a model of classification tone must extract not only accurate rules but comprehensible also, to help the human interpretation of the model and the decision make process. In this paper the authors describe a purely genetic approach, then a tabu search approach and finaly a memetic algorithm to extract classification rules. The memetic approach is a hybridization of a genetic algorithm (GA) and a local search based on a tabu search algorithm. Knowing that the amount of treated data is always huge in data mining applications, the authors propose to decrease the running time of the GA using a parallel scheme. In the authors' scheme the concept of generation has been removed and replaced by the cycle one and each individual owns a lifespan represented by a number of cycles affected to it randomly at its birth and at the end of which it disappears from the population. Consequently, only certain individuals of the population are evaluated within each iteration of the algorithm and not all our heterogeneous population. This causes the substantial reduction of the total running time of the algorithm since the evaluations of all individuals of each generation necessitates more than 80% of the total running time of a classical GA. This approach has been developed with the goal to present a new and efficient parallel scheme of the classical GA with better performances in terms of running time.
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An Overwiew Of Our Proposed Methods

In this paper, we are interested in the classification rules extraction problem and will construct a classification model using hybrid and parallel evolutionary methods. Indeed, evolutionary approaches (EAs) are well known for their great power to explore the search space. They present the ability of escaping from local optima due to their inherent global search capability. Their concurrent search enables them to promptly explore and identify new promising regions of the solution space. Though EAs are capable of global exploration and locating new regions of the solution space to identify potential candidates, however, when a potential region is identified, there is no further focus on the micro-exploitation aspect. Hence, local search are often used as a complement to EAs optimization that concentrate mainly on global exploration. For this, memetic algorithms, which incorporate local improvement search into EAs, are proposed (Ong & Keane, 2004). Experimental studies have shown that EA-Local Search (LS) hybrids or hybrid EAs are capable of more efficient search capabilities (Merz & Freisleben, 1999).

In a first step, our paper provides a memetic approach to extract a classification model which will be constituted by a set of rules presenting a high accuracy degree. Hence we will present all the modeling details of an extractor of classification rules based on a genetic algorithm (GA) firstly, then a Tabu Search (TS) to extract classification rules will be presented and finally a memetic approach GA-TS-based will be detailed to solve the same problem.

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