Customizing asynchronous parallel pattern search algorithm to improve ANN classifier for learning disabilities students identification

T. K. Wu, S. C. Huang, W. W. Chiou, Y. R. Meng

研究成果: Conference contribution

3 引文 斯高帕斯(Scopus)

摘要

Due to the implicit characteristics of learning disabilities (LD), the diagnosis of students with learning disabilities has been a difficult process that requires extensive man power and takes a long time. Through genetic-based parameters optimization, artificial neural network (ANN) classifier has proven to be a good predictor to the diagnosis of students with learning disabilities. In this study, we examine another optimization algorithm, the asynchronous parallel pattern search (APPS), to search for appropriate parameters in constructing ANN-based LD classifier. To fully take advantage of modern multi-cored CPU technologies and to further expand the potential search space, various modifications to both of the serial and parallel versions of the original APPS implementations have been developed. The outcomes show that APPS in its original implementation can be competitive to genetic algorithm in term of accuracy, while requiring much less execution time. Furthermore, with consecutive (two-step) applications of the modified APPS algorithm to fine-tune the ANN parameters, we have further improved the ANN-based LD identification accuracy as compared to our previous results using genetic algorithm.

原文English
主出版物標題Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
頁面1639-1643
頁數5
DOIs
出版狀態Published - 2011 十月 6
事件2011 7th International Conference on Natural Computation, ICNC 2011 - Shanghai, China
持續時間: 2011 七月 262011 七月 28

出版系列

名字Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
3

Other

Other2011 7th International Conference on Natural Computation, ICNC 2011
國家China
城市Shanghai
期間11-07-2611-07-28

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Neuroscience(all)

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