Improving ANN classification accuracy for the identification of students with LDs through evolutionary computation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Due to the implicit characteristics of learning disabilities (LD), the identification and diagnosis of students with learning disabilities has long been a difficult issue. Identification of LD usually requires extensive man power and takes a long time. Our previous studies using smaller size samples have shown that ANN classifier can be a good predictor to the identification of students with learning disabilities. In this study, we focus on the genetic algorithm based feature selection method and evolutionary computation based parameters optimization algorithm on a larger size data set to explore the potential limit that ANN can achieve in this specific classification problem. In addition, a different procedure in combining evolutionary algorithms and neural networks is proposed. We use the back propagation method to train the neural networks and derive a good combination of parameters. Evolutionary algorithm is then used to search around the neighborhood of the previously acquired parameters to further improve the classification accuracy. The outcomes show that the procedure is effective, and with appropriate combinations of features and parameters setting, the accuracy of the ANN classifier can reach the 88.2% mark, the best we have achieved so far. The result again indicates that AI techniques do have their roles on the identification of students with learning disabilities.

Original languageEnglish
Title of host publication2007 IEEE Congress on Evolutionary Computation, CEC 2007
Pages4358-4364
Number of pages7
DOIs
Publication statusPublished - 2007 Dec 1
Event2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore
Duration: 2007 Sep 252007 Sep 28

Publication series

Name2007 IEEE Congress on Evolutionary Computation, CEC 2007

Other

Other2007 IEEE Congress on Evolutionary Computation, CEC 2007
CountrySingapore
Period07-09-2507-09-28

Fingerprint

Disability
Evolutionary Computation
Evolutionary algorithms
Students
Classifiers
Neural networks
Evolutionary Algorithms
Backpropagation
Classifier
Feature extraction
Neural Networks
Genetic algorithms
Small Sample Size
Back Propagation
Parameter Optimization
Classification Problems
Feature Selection
Predictors
Optimization Algorithm
Learning

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

Cite this

Wu, T-K., Huang, S-C., & Meng, Y. R. (2007). Improving ANN classification accuracy for the identification of students with LDs through evolutionary computation. In 2007 IEEE Congress on Evolutionary Computation, CEC 2007 (pp. 4358-4364). [4425040] (2007 IEEE Congress on Evolutionary Computation, CEC 2007). https://doi.org/10.1109/CEC.2007.4425040
Wu, Tung-Kuang ; Huang, Shian-Chang ; Meng, Ying Ru. / Improving ANN classification accuracy for the identification of students with LDs through evolutionary computation. 2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. pp. 4358-4364 (2007 IEEE Congress on Evolutionary Computation, CEC 2007).
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abstract = "Due to the implicit characteristics of learning disabilities (LD), the identification and diagnosis of students with learning disabilities has long been a difficult issue. Identification of LD usually requires extensive man power and takes a long time. Our previous studies using smaller size samples have shown that ANN classifier can be a good predictor to the identification of students with learning disabilities. In this study, we focus on the genetic algorithm based feature selection method and evolutionary computation based parameters optimization algorithm on a larger size data set to explore the potential limit that ANN can achieve in this specific classification problem. In addition, a different procedure in combining evolutionary algorithms and neural networks is proposed. We use the back propagation method to train the neural networks and derive a good combination of parameters. Evolutionary algorithm is then used to search around the neighborhood of the previously acquired parameters to further improve the classification accuracy. The outcomes show that the procedure is effective, and with appropriate combinations of features and parameters setting, the accuracy of the ANN classifier can reach the 88.2{\%} mark, the best we have achieved so far. The result again indicates that AI techniques do have their roles on the identification of students with learning disabilities.",
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Wu, T-K, Huang, S-C & Meng, YR 2007, Improving ANN classification accuracy for the identification of students with LDs through evolutionary computation. in 2007 IEEE Congress on Evolutionary Computation, CEC 2007., 4425040, 2007 IEEE Congress on Evolutionary Computation, CEC 2007, pp. 4358-4364, 2007 IEEE Congress on Evolutionary Computation, CEC 2007, Singapore, 07-09-25. https://doi.org/10.1109/CEC.2007.4425040

Improving ANN classification accuracy for the identification of students with LDs through evolutionary computation. / Wu, Tung-Kuang; Huang, Shian-Chang; Meng, Ying Ru.

2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. p. 4358-4364 4425040 (2007 IEEE Congress on Evolutionary Computation, CEC 2007).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Wu T-K, Huang S-C, Meng YR. Improving ANN classification accuracy for the identification of students with LDs through evolutionary computation. In 2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. p. 4358-4364. 4425040. (2007 IEEE Congress on Evolutionary Computation, CEC 2007). https://doi.org/10.1109/CEC.2007.4425040