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Robust optimization of SVM hyperparameters in the classification of bioactive compounds

Overview of attention for article published in Journal of Cheminformatics, August 2015
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Title
Robust optimization of SVM hyperparameters in the classification of bioactive compounds
Published in
Journal of Cheminformatics, August 2015
DOI 10.1186/s13321-015-0088-0
Pubmed ID
Authors

Wojciech M Czarnecki, Sabina Podlewska, Andrzej J Bojarski

Abstract

Support Vector Machine has become one of the most popular machine learning tools used in virtual screening campaigns aimed at finding new drug candidates. Although it can be extremely effective in finding new potentially active compounds, its application requires the optimization of the hyperparameters with which the assessment is being run, particularly the C and [Formula: see text] values. The optimization requirement in turn, establishes the need to develop fast and effective approaches to the optimization procedure, providing the best predictive power of the constructed model. In this study, we investigated the Bayesian and random search optimization of Support Vector Machine hyperparameters for classifying bioactive compounds. The effectiveness of these strategies was compared with the most popular optimization procedures-grid search and heuristic choice. We demonstrated that Bayesian optimization not only provides better, more efficient classification but is also much faster-the number of iterations it required for reaching optimal predictive performance was the lowest out of the all tested optimization methods. Moreover, for the Bayesian approach, the choice of parameters in subsequent iterations is directed and justified; therefore, the results obtained by using it are constantly improved and the range of hyperparameters tested provides the best overall performance of Support Vector Machine. Additionally, we showed that a random search optimization of hyperparameters leads to significantly better performance than grid search and heuristic-based approaches. The Bayesian approach to the optimization of Support Vector Machine parameters was demonstrated to outperform other optimization methods for tasks concerned with the bioactivity assessment of chemical compounds. This strategy not only provides a higher accuracy of classification, but is also much faster and more directed than other approaches for optimization. It appears that, despite its simplicity, random search optimization strategy should be used as a second choice if Bayesian approach application is not feasible.Graphical abstractThe improvement of classification accuracy obtained after the application of Bayesian approach to the optimization of Support Vector Machines parameters.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 3%
Brazil 1 3%
Sweden 1 3%
Costa Rica 1 3%
Poland 1 3%
Unknown 34 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 36%
Researcher 7 18%
Student > Bachelor 6 15%
Other 4 10%
Student > Master 4 10%
Other 1 3%
Unknown 3 8%
Readers by discipline Count As %
Computer Science 12 31%
Engineering 8 21%
Chemistry 5 13%
Agricultural and Biological Sciences 3 8%
Medicine and Dentistry 2 5%
Other 4 10%
Unknown 5 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 August 2015.
All research outputs
#10,650,925
of 12,010,397 outputs
Outputs from Journal of Cheminformatics
#461
of 467 outputs
Outputs of similar age
#195,199
of 237,143 outputs
Outputs of similar age from Journal of Cheminformatics
#11
of 11 outputs
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