GA based CBR approach in Q&amp;A system

Fu, Yonggang; Shen, Ruimin

doi:10.1016/s0957-4174(03)00117-9

Cited by 40 publications

(22 citation statements)

References 9 publications

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“…Several recent works have applied GA-based CBR approach to different domains, including corporate bond rating (Shin & Han, 1999), housing customization (Juan, Shih, & Perng, 2006), Q&A system (Fu & Shen, 2004), e-learning system (Huang, Huang, & Chen, 2007), and etc.…”

Section: Case-based Reasoningmentioning

confidence: 99%

A hierarchical design of case-based reasoning in the balanced scorecard application

Yuan

Chiu

2009

Expert Systems with Applications

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Section: Case-based Reasoningmentioning

confidence: 99%

A hierarchical design of case-based reasoning in the balanced scorecard application

Yuan

Chiu

2009

Expert Systems with Applications

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“…The stopping criterion is set at the maximum number of generations in most cases, so our study also uses it as the stopping condition for GA search (Han and Kamber 2001;Chiu 2002;Fu and Shen 2004).…”

Section: Ga-optimization For K Parameter Of K-nnmentioning

confidence: 99%

Using genetic algorithms to optimize nearest neighbors for data mining

Ahn

Kim

2008

Ann Oper Res

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Case-based reasoning (CBR) is widely used in data mining for managerial applications because it often shows significant promise for improving the effectiveness of complex and unstructured decision making. There are, however, some limitations in designing appropriate case indexing and retrieval mechanisms including feature selection and feature weighting. Some of the prior studies pointed out that finding the optimal k parameter for the k-nearest neighbor (k-NN) is also one of the most important factors for designing an effective CBR system. Nonetheless, there have been few attempts to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. This study proposes a genetic algorithm (GA) approach to optimize the number of neighbors to combine. In this study, we apply this novel model to two real-world cases involving stock market and online purchase prediction problems. Experimental results show that a GA-optimized k-NN approach may outperform traditional k-NN. In addition, these results also show that our proposed method is as good as or sometime better than other AI techniques in performancecomparison.

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“…In mutation, with a very small mutation rate, arbitrarily selected bits in a chromosome are inverted. These steps of evolution continue until the stopping conditions are satisfied [5,6].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

“…In this study, the main goal is to find the optimal or near optimal parameters that produce the most accurate prediction solution. Thus, we set the fitness function for the test data set to the prediction accuracy of the test dataset [5,12,13].…”

Section: Phase 2 Trainingmentioning

confidence: 99%

Global Optimization of Support Vector Machines Using Genetic Algorithms for Bankruptcy Prediction

Ahn

Lee

Kim

2006

Neural Information Processing

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Abstract.One of the most important research issues in finance is building accurate corporate bankruptcy prediction models since they are essential for the risk management of financial institutions. Thus, researchers have applied various data-driven approaches to enhance prediction performance including statistical and artificial intelligence techniques. Recently, support vector machines (SVMs) are becoming popular because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. In addition, they don't require huge training samples and have little possibility of overfitting. However, in order to use SVM, a user should determine several factors such as the parameters of a kernel function, appropriate feature subset, and proper instance subset by heuristics, which hinders accurate prediction results when using SVM. In this study, we propose a novel approach to enhance the prediction performance of SVM for the prediction of financial distress. Our suggestion is the simultaneous optimization of the feature selection and the instance selection as well as the parameters of a kernel function for SVM by using genetic algorithms (GAs). We apply our model to a real-world case. Experimental results show that the prediction accuracy of conventional SVM may be improved significantly by using our model.

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GA based CBR approach in Q&A system

Cited by 40 publications

References 9 publications

A hierarchical design of case-based reasoning in the balanced scorecard application

A hierarchical design of case-based reasoning in the balanced scorecard application

Using genetic algorithms to optimize nearest neighbors for data mining

Global Optimization of Support Vector Machines Using Genetic Algorithms for Bankruptcy Prediction

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