AlPOs Synthetic Factor Analysis Based on Maximum Weight and Minimum Redundancy Feature Selection
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 (Al...
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doaj-ed8bf874ec774a42adbb864a84a7cdb02020-11-24T23:26:35ZengMDPI AGInternational Journal of Molecular Sciences1422-00672013-11-011411221322214810.3390/ijms141122132AlPOs Synthetic Factor Analysis Based on Maximum Weight and Minimum Redundancy Feature SelectionYinghua LvMiao QiMing ZhangNa GaoJun KongYuting GuoJianzhong WangThe 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.http://www.mdpi.com/1422-0067/14/11/22132AlPOsdata miningfeature selectionrational synthesis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yinghua Lv Miao Qi Ming Zhang Na Gao Jun Kong Yuting Guo Jianzhong Wang |
spellingShingle |
Yinghua Lv Miao Qi Ming Zhang Na Gao Jun Kong Yuting Guo Jianzhong Wang AlPOs Synthetic Factor Analysis Based on Maximum Weight and Minimum Redundancy Feature Selection International Journal of Molecular Sciences AlPOs data mining feature selection rational synthesis |
author_facet |
Yinghua Lv Miao Qi Ming Zhang Na Gao Jun Kong Yuting Guo Jianzhong Wang |
author_sort |
Yinghua Lv |
title |
AlPOs Synthetic Factor Analysis Based on Maximum Weight and Minimum Redundancy Feature Selection |
title_short |
AlPOs Synthetic Factor Analysis Based on Maximum Weight and Minimum Redundancy Feature Selection |
title_full |
AlPOs Synthetic Factor Analysis Based on Maximum Weight and Minimum Redundancy Feature Selection |
title_fullStr |
AlPOs Synthetic Factor Analysis Based on Maximum Weight and Minimum Redundancy Feature Selection |
title_full_unstemmed |
AlPOs Synthetic Factor Analysis Based on Maximum Weight and Minimum Redundancy Feature Selection |
title_sort |
alpos synthetic factor analysis based on maximum weight and minimum redundancy feature selection |
publisher |
MDPI AG |
series |
International Journal of Molecular Sciences |
issn |
1422-0067 |
publishDate |
2013-11-01 |
description |
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. |
topic |
AlPOs data mining feature selection rational synthesis |
url |
http://www.mdpi.com/1422-0067/14/11/22132 |
work_keys_str_mv |
AT yinghualv alpossyntheticfactoranalysisbasedonmaximumweightandminimumredundancyfeatureselection AT miaoqi alpossyntheticfactoranalysisbasedonmaximumweightandminimumredundancyfeatureselection AT mingzhang alpossyntheticfactoranalysisbasedonmaximumweightandminimumredundancyfeatureselection AT nagao alpossyntheticfactoranalysisbasedonmaximumweightandminimumredundancyfeatureselection AT junkong alpossyntheticfactoranalysisbasedonmaximumweightandminimumredundancyfeatureselection AT yutingguo alpossyntheticfactoranalysisbasedonmaximumweightandminimumredundancyfeatureselection AT jianzhongwang alpossyntheticfactoranalysisbasedonmaximumweightandminimumredundancyfeatureselection |
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1725554497400340480 |