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...
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| Published in | Chemical product and process modeling Vol. 7; no. 1 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
De Gruyter
31.07.2012
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1934-2659 2194-6159 1934-2659 |
| DOI | 10.1515/1934-2659.1645 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Tadé, Moses O. Vuthaluru, Hari Saptoro, Agus |
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| Title | A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models |
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