A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models
This paper proposes a method, namely MDKS (Kennard-Stone algorithm based on Mahalanobis distance), to divide the data into training and testing subsets for developing artificial neural network (ANN) models. This method is a modified version of the Kennard-Stone (KS) algorithm. With this method, bett...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
The Berkeley Electronic Press
2012
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/45101 |
| _version_ | 1848757189356814336 |
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| author | Saptoro, Agus Tade, Moses Vuthaluru, Hari |
| author_facet | Saptoro, Agus Tade, Moses Vuthaluru, Hari |
| author_sort | Saptoro, Agus |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper proposes a method, namely MDKS (Kennard-Stone algorithm based on Mahalanobis distance), to divide the data into training and testing subsets for developing artificial neural network (ANN) models. This method is a modified version of the Kennard-Stone (KS) algorithm. With this method, better data splitting, in terms of data representation and enhanced performance of developed ANN models, can be achieved. Compared with standard KS algorithm and another improved KS algorithm (data division based on joint x - y distances (SPXY) method), the proposed method has also shown a better performance. Therefore, the proposed technique can be used as an advantageous alternative to other existing methods of data splitting for developing ANN models. Care should be taken when dealing with large amount of dataset since they may increase the computational load for MDKS due to its variance-covariance matrix calculations. |
| first_indexed | 2025-11-14T09:24:08Z |
| format | Journal Article |
| id | curtin-20.500.11937-45101 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:24:08Z |
| publishDate | 2012 |
| publisher | The Berkeley Electronic Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-451012017-09-13T14:19:37Z A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models Saptoro, Agus Tade, Moses Vuthaluru, Hari data division kennard-stone algorithm ANN models MDKS This paper proposes a method, namely MDKS (Kennard-Stone algorithm based on Mahalanobis distance), to divide the data into training and testing subsets for developing artificial neural network (ANN) models. This method is a modified version of the Kennard-Stone (KS) algorithm. With this method, better data splitting, in terms of data representation and enhanced performance of developed ANN models, can be achieved. Compared with standard KS algorithm and another improved KS algorithm (data division based on joint x - y distances (SPXY) method), the proposed method has also shown a better performance. Therefore, the proposed technique can be used as an advantageous alternative to other existing methods of data splitting for developing ANN models. Care should be taken when dealing with large amount of dataset since they may increase the computational load for MDKS due to its variance-covariance matrix calculations. 2012 Journal Article http://hdl.handle.net/20.500.11937/45101 10.1515/1934-2659.1645 The Berkeley Electronic Press fulltext |
| spellingShingle | data division kennard-stone algorithm ANN models MDKS Saptoro, Agus Tade, Moses Vuthaluru, Hari A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models |
| title | A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models |
| title_full | A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models |
| title_fullStr | A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models |
| title_full_unstemmed | A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models |
| title_short | A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models |
| title_sort | modified kennard-stone algorithm for optimal division of data for developing artificial neural network models |
| topic | data division kennard-stone algorithm ANN models MDKS |
| url | http://hdl.handle.net/20.500.11937/45101 |