We present the results of an information theory-based approach to select an optimal subset of features for the prediction of protein model quality. The optimal subset of features was calculated by means of a backward selection procedure. The performances of a probabilistic classifier modeled by means of a Kernel Probability Density Estimation method (KPDE) were compared with those of a feed-forward Artificial Neural Network (ANN) and a Support Vector Machine (SVM).

An Information Theoretic Approach for Improving Data Driven Prediction of Protein Model Quality / Montuori, Alfonso; Raimondo, Giovanni; Pasero, Eros Gian Alessandro. - In: COMPUTERS & MATHEMATICS WITH APPLICATIONS. - ISSN 0898-1221. - 55:(2008), pp. 997-1006. [10.1016/j.camwa.2006.12.096]

An Information Theoretic Approach for Improving Data Driven Prediction of Protein Model Quality

MONTUORI, Alfonso;RAIMONDO, Giovanni;PASERO, Eros Gian Alessandro
2008

Abstract

We present the results of an information theory-based approach to select an optimal subset of features for the prediction of protein model quality. The optimal subset of features was calculated by means of a backward selection procedure. The performances of a probabilistic classifier modeled by means of a Kernel Probability Density Estimation method (KPDE) were compared with those of a feed-forward Artificial Neural Network (ANN) and a Support Vector Machine (SVM).
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/1534782
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