AI Bibliography
AI Bibliography |
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Tan, C. J., Lim, C. P., & Cheah, Y.-.-N. (2014). A multi-objective evolutionary algorithm-based ensemble optimizer for feature selection and classification with neural network models. Neurocomputing, 125, 217–228. |
| Resource type: Journal Article BibTeX citation key: Tan2014 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Computer Science, Data Sciences, Decision Theory, General, Neuroscience Subcategories: Big data, Decision making, Informatics, Machine learning, Machine recognition, Neural nets Creators: Cheah, Lim, Tan Publisher: Collection: Neurocomputing |
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| Abstract |
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In this paper, we propose a new multi-objective evolutionary algorithm-based ensemble optimizer coupled with neural network models for undertaking feature selection and classification problems. Specifically, the Modified micro Genetic Algorithm (MmGA) is used to form the ensemble optimizer. The aim of the MmGA-based ensemble optimizer is two-fold, i.e. to select a small number of input features for classification and to improve the classification performances of neural network models. To evaluate the effectiveness of the proposed system, a number of benchmark problems are first used, and the results are compared with those from other methods. The applicability of the proposed system to a human motiondetection and classification task is then evaluated. The outcome positively demonstrates that the proposed MmGA-based ensemble optimizer is able to improve the classification performances of neural network models with a smaller number of input features.
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