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Publication
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| Title of Article |
Optimal data division for empowering artificial neural network models employing a modified M-SPXY algorithm |
| Date of Acceptance |
18 June 2019 |
| Journal |
| Title of Journal |
Engineering and Applied science Research (EASR) |
| Standard |
SCOPUS |
| Institute of Journal |
Engineering and Applied Science Research (EASR) Faculty of Engineering, Khon Kaen University |
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| Year of Publication |
2019 |
| Page |
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| Abstract |
Data splitting is an important step in artificial neural network (ANN) models, which is found in the form of training and testing subsets. In general, random data splitting method is favored to divide a pool of samples into subsets, without considering the quality of data for trains in process step of a neural network. The drawback from poor data splitting methods, poses ill effects to the performance of the neural network; when the data involves complex matrices or multivariate modeling. In order to overcome this drawback, this paper presents our proposed M-SPXY method; based on a modified version of Sample Set Partitioning, based on joint X-y distances (SPXY) method. The proposed method has resulted in a better performance, compared to the modified Kennard-Stone (KS) method, based on Mahalanobis distances (MDKS). In our experiments, the proposed approach was used to compare different data splitting methods using data sets from the repository of the University of California in Irvine (UCI), processed through an Extreme Learning Machine (ELM) neural network. Performance is measured in terms of classification accuracy. The results indicate that the classification accuracy of the proposed M-SPXY is superior to that of the MDKS data splitting method. |
| Keyword |
Extreme learning machine, SPXY, Neural network, Subset selection, Mahalanobis distance, Classification |
| Author |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ได้รับการตอบรับให้ตีพิมพ์ |
| Level of Publication |
นานาชาติ |
| citation |
false |
| Part of thesis |
false |
| Attach file |
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| Citation |
0
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Journal Publication
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