A novel classification method based on the ensemble learning and feature selection for aluminophosphate structural prediction

• A novel classification algorithm is proposed.
• A novel feature selection model is proposed.
• The prediction and selected synthetic factors can reach a good performance.
• The result of feature selection can provides guidance for synthesis of microporous materials.

In this paper, a novel classification algorithm based on the ensemble learning and feature selection is proposed for predicting the specific microporous aluminophosphate ring structure. The proposed method can select the most significant synthetic factors for the generation of (6, 12)-ring-containing structure. First, the clustering method is employed for making each training subset contains all the structural characteristics of samples. Then, the method takes full account of the discrimination and class information of each feature by calculating the scores. Specially, the scores are fused for getting a weight for each feature. Finally, we select the significant features according to the weights. The result of feature selection will help to predict the (6, 12)-ring-containing AlPO structure well. Moreover, we compare our method with several classical feature selection methods and classification method by theoretical analysis and extensive experiments. Experimental results show that our method can achieve higher predictive accuracy with less synthetic factors.

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