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Molecular activity prediction by means of supervised subspace projection based ensembles of classifiers

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This paper proposes a method for molecular activity prediction in QSAR studies using ensembles of classifiers constructed by means of two supervised subspace projection methods, namely nonparametric discriminant analysis (NDA) and hybrid discriminant analysis (HDA). We studied the performance of the proposed ensembles compared to classical ensemble methods using four molecular datasets and eight different models for the representation of the molecular structure. Using several measures and statistical tests for classifier comparison, we observe that our proposal improves the classification results with respect to classical ensemble methods. Therefore, we show that ensembles constructed using supervised subspace projections offer an effective way of creating classifiers in cheminformatics.
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Keywords: QSAR; classifier ensembles; machine learning; molecular activity predictions; supervised subspace projection methods

Document Type: Research Article

Affiliations: Department of Computing and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, Córdoba, Spain

Publication date: March 4, 2018

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