A two-step supervised learning artificial neural network for imbalanced dataset problems
In this paper, a two-step supervised learning algorithm of a single layer feedforward Articial Neural Network (ANN) is proposed for solving imbalanced dataset problems. Levenberg Marquart backpropagation learning algorithm is utilized in the first step learning, while the second step learning mechan...
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my.utm.465432017-09-12T08:31:10Z http://eprints.utm.my/id/eprint/46543/ A two-step supervised learning artificial neural network for imbalanced dataset problems Adam, Asrul Ibrahim, Zuwairie Shapiai, Mohd. Ibrahim Lim, Chun Chew Lee, Wen Jau Khalid, Marzuki Watada, Junzo QA76 Computer software In this paper, a two-step supervised learning algorithm of a single layer feedforward Articial Neural Network (ANN) is proposed for solving imbalanced dataset problems. Levenberg Marquart backpropagation learning algorithm is utilized in the first step learning, while the second step learning mechanism is introduced by optimizing the decision threshold of the step function at the output layer of ANN using particle swarm optimization (PSO). After all the steps learning are accomplished, the best weights and decision threshold value are obtained to be used for testing process. Several imbalanced datasets, which are available in UCI Machine Learning Repository, are chosen as case study. The prediction performance is assessed by Geometric Mean (G-mean), which is a standard measure to indicate the efficiency of classier for imbalanced datasets. Based on the experimental results, the proposed method is able to provide good G-mean value compared with the conventional ANN approaches. 2012 Article PeerReviewed Adam, Asrul and Ibrahim, Zuwairie and Shapiai, Mohd. Ibrahim and Lim, Chun Chew and Lee, Wen Jau and Khalid, Marzuki and Watada, Junzo (2012) A two-step supervised learning artificial neural network for imbalanced dataset problems. International Journal of Innovative Computing, Information and Control, 8 (5A). pp. 3163-3172. ISSN 1349-4198 |
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QA76 Computer software Adam, Asrul Ibrahim, Zuwairie Shapiai, Mohd. Ibrahim Lim, Chun Chew Lee, Wen Jau Khalid, Marzuki Watada, Junzo A two-step supervised learning artificial neural network for imbalanced dataset problems |
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In this paper, a two-step supervised learning algorithm of a single layer feedforward Articial Neural Network (ANN) is proposed for solving imbalanced dataset problems. Levenberg Marquart backpropagation learning algorithm is utilized in the first step learning, while the second step learning mechanism is introduced by optimizing the decision threshold of the step function at the output layer of ANN using particle swarm optimization (PSO). After all the steps learning are accomplished, the best weights and decision threshold value are obtained to be used for testing process. Several imbalanced datasets, which are available in UCI Machine Learning Repository, are chosen as case study. The prediction performance is assessed by Geometric Mean (G-mean), which is a standard measure to indicate the efficiency of classier for imbalanced datasets. Based on the experimental results, the proposed method is able to provide good G-mean value compared with the conventional ANN approaches. |
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Article |
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Adam, Asrul Ibrahim, Zuwairie Shapiai, Mohd. Ibrahim Lim, Chun Chew Lee, Wen Jau Khalid, Marzuki Watada, Junzo |
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Adam, Asrul Ibrahim, Zuwairie Shapiai, Mohd. Ibrahim Lim, Chun Chew Lee, Wen Jau Khalid, Marzuki Watada, Junzo |
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Adam, Asrul |
title |
A two-step supervised learning artificial neural network for imbalanced dataset problems |
title_short |
A two-step supervised learning artificial neural network for imbalanced dataset problems |
title_full |
A two-step supervised learning artificial neural network for imbalanced dataset problems |
title_fullStr |
A two-step supervised learning artificial neural network for imbalanced dataset problems |
title_full_unstemmed |
A two-step supervised learning artificial neural network for imbalanced dataset problems |
title_sort |
two-step supervised learning artificial neural network for imbalanced dataset problems |
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2012 |
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http://eprints.utm.my/id/eprint/46543/ |
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1643652066350989312 |