Feature selection method based on fuzzy entropy for regression in QSAR studies |
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Authors: | Zahra Elmi Karim Faez Mohammad Goodarzi |
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Institution: | 1. Department of Artificial Intelligent Engineering , Islamic Azad University of Qazvin , Qazvin, Iran;2. Electrical Engineering Department , Amirkabir University of Technology (Tehran Polytechnic) , Hafez Avenue, Tehran, Iran, 15914;3. Department of Chemistry , Faculty of Sciences, Azad University of Arak , Arak, Iran;4. Young Researchers Club , Azad University of Arak , Arak, Iran |
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Abstract: | Feature selection and feature extraction are the most important steps in classification and regression systems. Feature selection is commonly used to reduce the dimensionality of datasets with tens or hundreds of thousands of features, which would be impossible to process further. Recent example includes quantitative structure–activity relationships (QSAR) dataset including 1226 features. A major problem of QSAR is the high dimensionality of the feature space; therefore, feature selection is the most important step in this study. This paper presents a novel feature selection algorithm that is based on entropy. The performance of the proposed algorithm is compared with that of a genetic algorithm method and a stepwise regression method. The root mean square error of prediction in a QSAR study using entropy, genetic algorithm and stepwise regression using multiple linear regressions model for training set and test set were 0.3433, 0.3591 and 0.5500, 0.4326 and 0.6373, 0.6672, respectively. |
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Keywords: | fuzzy entropy feature selection quantitative structure–activity relationships regression genetic algorithm multiple linear regressions |
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