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Int. J. Mol. Sci. 2013, 14(11), 22132-22148; doi:10.3390/ijms141122132

AlPOs Synthetic Factor Analysis Based on Maximum Weight and Minimum Redundancy Feature Selection

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Received: 25 September 2013 / Revised: 23 October 2013 / Accepted: 23 October 2013 / Published: 8 November 2013
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The relationship between synthetic factors and the resulting structures is critical for rational synthesis of zeolites and related microporous materials. In this paper, we develop a new feature selection method for synthetic factor analysis of (6,12)-ring-containing microporous aluminophosphates (AlPOs). The proposed method is based on a maximum weight and minimum redundancy criterion. With the proposed method, we can select the feature subset in which the features are most relevant to the synthetic structure while the redundancy among these selected features is minimal. Based on the database of AlPO synthesis, we use (6,12)-ring-containing AlPOs as the target class and incorporate 21 synthetic factors including gel composition, solvent and organic template to predict the formation of (6,12)-ring-containing microporous aluminophosphates (AlPOs). From these 21 features, 12 selected features are deemed as the optimized features to distinguish (6,12)-ring-containing AlPOs from other AlPOs without such rings. The prediction model achieves a classification accuracy rate of 91.12% using the optimal feature subset. Comprehensive experiments demonstrate the effectiveness of the proposed algorithm, and deep analysis is given for the synthetic factors selected by the proposed method.
Keywords: AlPOs; data mining; feature selection; rational synthesis AlPOs; data mining; feature selection; rational synthesis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Guo, Y.; Wang, J.; Gao, N.; Qi, M.; Zhang, M.; Kong, J.; Lv, Y. AlPOs Synthetic Factor Analysis Based on Maximum Weight and Minimum Redundancy Feature Selection. Int. J. Mol. Sci. 2013, 14, 22132-22148.

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