MEPAR-miner: Multi-expression programming for classification rule mining

Baykasoğlu, Adil; Özbakır, Lale

doi:10.1016/j.ejor.2006.10.015

Cited by 38 publications

(27 citation statements)

References 21 publications

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“…This spans a wide area of algorithms including evolutionary algorithms, evolutionary programming, genetic algorithms, genetic programming, swarm intelligence, cultural algorithms, etc. Evolutionary algorithms perform a global search and are convenient for parallelization (Baykasoglu and Ozbakir 2007). They are robust search methods that adapt to the environment and can discover interesting knowledge that will be missed by greedy algorithms (Freitas 2007).…”

Section: Evolutionary Computing For Solving Multi Objective Optimizatmentioning

confidence: 99%

Evolutionary multi objective optimization for rule mining: a review

Srinivasan¹,

Ramakrishnan²

2011

Artif Intell Rev

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Evolutionary multi objective optimization (EMOO) systems are evolutionary systems which are used for optimizing various measures of the evolving system. Rule mining has gained attention in the knowledge discovery literature. The problem of discovering rules with specific properties is treated as a multi objective optimization problem. The objectives to be optimized being the metrics like accuracy, comprehensibility, surprisingness, novelty to name a few. There are a variety of EMOO algorithms in the literature. The performance of these EMOO algorithms is influenced by various characteristics including evolutionary technique used, chromosome representation, parameters like population size, number of generations, crossover rate, mutation rate, stopping criteria, Reproduction operators used, objectives taken for optimization, the fitness function used, optimization strategy, the type of data, number of class attributes and the area of application. This study reviews EMOO systems taking the above criteria into consideration. There are other hybridization strategies like use of intelligent agents, fuzzification, meta data and meta heuristics, parallelization, interactiveness with the user, visualization, etc., which further enhance the performance and usability of the system. Genetic Algorithms (GAs) and Genetic Programming (GPs) are two widely used evolutionary strategies for rule knowledge discovery in Data mining. Thus the proposed study aims at studying the various characteristics of the EMOO systems taking into consideration the two evolutionary strategies of Genetic Algorithm and Genetic programming.

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Section: Evolutionary Computing For Solving Multi Objective Optimizatmentioning

confidence: 99%

Evolutionary multi objective optimization for rule mining: a review

Srinivasan¹,

Ramakrishnan²

2011

Artif Intell Rev

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“…When a rule is used to classify a given training instance, one of the four possible concepts can be observed [31]: [2].…”

Section: Classification Rule Extraction From Neural Network Via Tacomentioning

confidence: 99%

“…An effective defect diagnosis can result in low-cost and highquality products. Here, data mining (DM) approach can be very useful as it is one of the best choices for analyzing vast amount of operational data [2,3].…”

Section: Introductionmentioning

confidence: 99%

Rule extraction from artificial neural networks to discover causes of quality defects in fabric production

Özbakır

Baykasoğlu

Kulluk

2010

Neural Comput & Applic

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In this paper, a novel classification rule extraction algorithm which has been recently proposed by authors is employed to determine the causes of quality defects in a fabric production facility in terms of predetermined parameters like machine type, warp type etc. The proposed rule extraction algorithm works on the trained artificial neural networks in order to discover the hidden information which is available in the form of connection weights in them. The proposed algorithm is mainly based on a swarm intelligence metaheuristic which is known as Touring Ant Colony Optimization (TACO). The algorithm has a hierarchical structure with two levels. In the first level, a multilayer perceptron type neural network is trained and its weights are extracted. After obtaining the weights, in the second level, the TACO-based algorithm is applied to extract classification rules. The main purpose of the present work is to determine and analyze the most effective parameters on the quality defects in fabric production. The parameters and their levels which give the best quality results are tried to be discovered and evaluated by making use of the proposed algorithm. It is also aimed to compare the accuracy of proposed algorithm with several other rulebased algorithms in order to present its competitiveness.

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“…For example sensitivity and specificity are rule metrics which are used in medical domain while precision and recall are measures used in information retrieval problems. MEPAR-miner (Multi-Expression Programming for Association Rule Mining) for rule induction is proposed in [4] which uses sensitivity and specificity of rules to define their fitness function. Reynolds et al, [15] describe the application of a multi-objective Greedy Randomized Search Procedure to rule selection, where previously generated simple rules are combined to give rule sets that minimize complexity and misclassification cost.…”

Section: Related Workmentioning

confidence: 99%

“…Evolutionary algorithms perform a global search and are convenient for parallelization [4]. They are robust search methods that adapt to the environment and can discover interesting knowledge that will be missed by greedy algorithms [5].…”

Section: Related Workmentioning

confidence: 99%