Extracting rules from trained neural network using GA for managing E-business

El-alfi, A. E.; Haque, Rafiqul; ElAlami, M. E.

doi:10.1016/j.asoc.2003.08.004

Cited by 42 publications

(18 citation statements)

References 6 publications

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“…It is a stochastic method that can be easily applied to nonlinear and noncontinuous functions, and it only requires a zeroth-order derivative [14]. GA has been successfully used to solve complex nonlinear problems in highly diverse fields [15][16][17][18][19][20], including media optimization [21,22].…”

Section: Introductionmentioning

confidence: 99%

Media optimization for biosurfactant production by Rhodococcus erythropolis MTCC 2794: artificial intelligence versus a statistical approach

Pal

Vaidya

Desai³

et al. 2009

J Ind Microbiol Biotechnol

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This paper entails a comprehensive study on production of a biosurfactant from Rhodococcus erythropolis MTCC 2794. Two optimization techniques--(1) artificial neural network (ANN) coupled with genetic algorithm (GA) and (2) response surface methodology (RSM)--were used for media optimization in order to enhance the biosurfactant yield by Rhodococcus erythropolis MTCC 2794. ANN and RSM models were developed, incorporating the quantity of four medium components (sucrose, yeast extract, meat peptone, and toluene) as independent input variables and biosurfactant yield [calculated in terms of percent emulsification index (% EI(24))] as output variable. ANN-GA and RSM were compared for their predictive and generalization ability using a separate data set of 16 experiments, for which the average quadratic errors were approximately 3 and approximately 6%, respectively. ANN-GA was found to be more accurate and consistent in predicting optimized conditions and maximum yield than RSM. For the ANN-GA model, the values of correlation coefficient and average quadratic error were approximately 0.99 and approximately 3%, respectively. It was also shown that ANN-based models could be used accurately for sensitivity analysis. ANN-GA-optimized media gave about a 3.5-fold enhancement in biosurfactant yield.

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Section: Introductionmentioning

confidence: 99%

Media optimization for biosurfactant production by Rhodococcus erythropolis MTCC 2794: artificial intelligence versus a statistical approach

Pal

Vaidya

Desai³

et al. 2009

J Ind Microbiol Biotechnol

View full text Add to dashboard Cite

show abstract

“…Experimental results were proved that their approach generates rules that are more accurate than the existing methods based on decision trees and linear regression. Elalfi et al [21] presented a new algorithm for extracting accurate and comprehensible rules from databases via trained NN using GA. Their algorithm does not depend on the NN training algorithms and does not modify the training results. The GA is used to find the optimal values of input attributes which maximize the output function of output nodes.…”

Section: Classification Rule Extraction From Neural Network Via Tacomentioning

confidence: 99%

“…For extracting rules between input attributes and related classes, it is necessary to find the input vector, which maximizes the function v k . This is an optimization problem which can be stated as [15,16,21]:…”

Section: Classification Rule Extraction From Neural Network Via Tacomentioning

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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“…Elalfi et al [21] proposed an algorithm for extracting accurate and intelligible rules from databases via a trained artificial neural network using genetic algorithms. Kahramanli and Allahverdi [22] presented a method that extracts rules from trained adaptive neural networks using artificial immune systems.…”

Section: Introductionmentioning

confidence: 99%

A Rule Induction Algorithm for Knowledge Discovery and Classification

Akgöbek¹

2013

Turk J Elec Eng & Comp Sci

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Classification and rule induction are key topics in the fields of decision making and knowledge discovery.The objective of this study is to present a new algorithm developed for automatic knowledge acquisition in data mining.The proposed algorithm has been named RES-2 (Rule Extraction System). It aims at eliminating the pitfalls and disadvantages of the techniques and algorithms currently in use. The proposed algorithm makes use of the direct rule extraction approach, rather than the decision tree. For this purpose, it uses a set of examples to induce general rules. In this study, 15 datasets consisting of multiclass values with different properties and sizes and obtained from the University of California, Irvine, have been used. Classification accuracy and rule count have been used to test the proposed method. This method presents an alternative 3-step method to classify categorical, binary, and continuous data by taking advantage of algorithms for data mining classification and decision rule generation. The method aims at improving the classification accuracy of the algorithms that extract the decision rules. Experimental studies were conducted on the benchmark datasets and the results of the comparisons with some known algorithms for decision rule generation have shown that the proposed method performs classification with a higher accuracy and generates fewer rules.

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Extracting rules from trained neural network using GA for managing E-business

Cited by 42 publications

References 6 publications

Media optimization for biosurfactant production by Rhodococcus erythropolis MTCC 2794: artificial intelligence versus a statistical approach

Media optimization for biosurfactant production by Rhodococcus erythropolis MTCC 2794: artificial intelligence versus a statistical approach

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

A Rule Induction Algorithm for Knowledge Discovery and Classification

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