Effects of feature selection on the identification of students with learning disabilities using ANN

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

3 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 involves interpreting some standard tests or checklist scores and comparing them to norms that are derived from statistical method. In our previous study, we made a first attempt in adopting two well-known artificial intelligence techniques, namely, artificial neural network (ANN) and support vector machine (SVM), to the LD identification problem. The preliminary results are quite satisfactory, and indicate that we may be going in the right direction. In this paper, we go one step further by combining various feature selection algorithms and the ANN model. The outcomes show that the correct identification rate has improved quite a lot over what we achieved previously. The combined selected features and the ANN classifier can be used as a strong indicator in the LD identification process and improve the accuracy of diagnosis.

Original languageEnglish
Title of host publicationAdvances in Natural Computation - Second International Conference, ICNC 2006, Proceedings,
PublisherSpringer Verlag
Pages565-574
Number of pages10
ISBN (Print)3540459014, 9783540459019
Publication statusPublished - 2006 Jan 1
Event2nd International Conference on Natural Computation, ICNC 2006 - Xi'an, China
Duration: 2006 Sep 242006 Sep 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4221 LNCS - I
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Conference on Natural Computation, ICNC 2006
CountryChina
CityXi'an
Period06-09-2406-09-28

Fingerprint

Disability
Feature Selection
Artificial Neural Network
Feature extraction
Students
Neural networks
Artificial intelligence
Support vector machines
Statistical methods
Identification (control systems)
Classifiers
Identification Problem
Neural Network Model
Statistical method
Artificial Intelligence
Support Vector Machine
Classifier
Learning
Norm

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Wu, T-K., Huang, S-C., & Meng, Y. R. (2006). Effects of feature selection on the identification of students with learning disabilities using ANN. In Advances in Natural Computation - Second International Conference, ICNC 2006, Proceedings, (pp. 565-574). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4221 LNCS - I). Springer Verlag.
Wu, Tung-Kuang ; Huang, Shian-Chang ; Meng, Ying Ru. / Effects of feature selection on the identification of students with learning disabilities using ANN. Advances in Natural Computation - Second International Conference, ICNC 2006, Proceedings,. Springer Verlag, 2006. pp. 565-574 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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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 involves interpreting some standard tests or checklist scores and comparing them to norms that are derived from statistical method. In our previous study, we made a first attempt in adopting two well-known artificial intelligence techniques, namely, artificial neural network (ANN) and support vector machine (SVM), to the LD identification problem. The preliminary results are quite satisfactory, and indicate that we may be going in the right direction. In this paper, we go one step further by combining various feature selection algorithms and the ANN model. The outcomes show that the correct identification rate has improved quite a lot over what we achieved previously. The combined selected features and the ANN classifier can be used as a strong indicator in the LD identification process and improve the accuracy of diagnosis.",
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Wu, T-K, Huang, S-C & Meng, YR 2006, Effects of feature selection on the identification of students with learning disabilities using ANN. in Advances in Natural Computation - Second International Conference, ICNC 2006, Proceedings,. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4221 LNCS - I, Springer Verlag, pp. 565-574, 2nd International Conference on Natural Computation, ICNC 2006, Xi'an, China, 06-09-24.

Effects of feature selection on the identification of students with learning disabilities using ANN. / Wu, Tung-Kuang; Huang, Shian-Chang; Meng, Ying Ru.

Advances in Natural Computation - Second International Conference, ICNC 2006, Proceedings,. Springer Verlag, 2006. p. 565-574 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4221 LNCS - I).

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

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Wu T-K, Huang S-C, Meng YR. Effects of feature selection on the identification of students with learning disabilities using ANN. In Advances in Natural Computation - Second International Conference, ICNC 2006, Proceedings,. Springer Verlag. 2006. p. 565-574. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).