The University of St Andrews

Research@StAndrews:FullText >
University of St Andrews Research >
University of St Andrews Research >
University of St Andrews Research >

Please use this identifier to cite or link to this item:
View Statistics

Files in This Item:

File Description SizeFormat
oboyle21.pdf646.02 kBAdobe PDFView/Open
Title: Simultaneous feature selection and parameter optimisation using an artificial ant colony : case study of melting point prediction
Authors: O'Boyle, NM
Palmer, DS
Nigsch, F
Mitchell, John Blayney Owen
Keywords: QD Chemistry
Issue Date: 29-Oct-2008
Citation: O'Boyle , N M , Palmer , D S , Nigsch , F & Mitchell , J B O 2008 , ' Simultaneous feature selection and parameter optimisation using an artificial ant colony : case study of melting point prediction ' Chemistry Central Journal , vol 2 , 21 . , 10.1186/1752-153X-2-21
Abstract: Background We present a novel feature selection algorithm, Winnowing Artificial Ant Colony (WAAC), that performs simultaneous feature selection and model parameter optimisation for the development of predictive quantitative structure-property relationship (QSPR) models. The WAAC algorithm is an extension of the modified ant colony algorithm of Shen et al. (J Chem Inf Model 2005, 45: 1024–1029). We test the ability of the algorithm to develop a predictive partial least squares model for the Karthikeyan dataset (J Chem Inf Model 2005, 45: 581–590) of melting point values. We also test its ability to perform feature selection on a support vector machine model for the same dataset. Results Starting from an initial set of 203 descriptors, the WAAC algorithm selected a PLS model with 68 descriptors which has an RMSE on an external test set of 46.6°C and R2 of 0.51. The number of components chosen for the model was 49, which was close to optimal for this feature selection. The selected SVM model has 28 descriptors (cost of 5, ε of 0.21) and an RMSE of 45.1°C and R2 of 0.54. This model outperforms a kNN model (RMSE of 48.3°C, R2 of 0.47) for the same data and has similar performance to a Random Forest model (RMSE of 44.5°C, R2 of 0.55). However it is much less prone to bias at the extremes of the range of melting points as shown by the slope of the line through the residuals: -0.43 for WAAC/SVM, -0.53 for Random Forest. Conclusion With a careful choice of objective function, the WAAC algorithm can be used to optimise machine learning and regression models that suffer from overfitting. Where model parameters also need to be tuned, as is the case with support vector machine and partial least squares models, it can optimise these simultaneously. The moving probabilities used by the algorithm are easily interpreted in terms of the best and current models of the ants, and the winnowing procedure promotes the removal of irrelevant descriptors.
Version: Publisher PDF
Description: The authors thank the BBSRC (NMOB and JBOM – grant BB/C51320X/1), Pfizer (DSP and JBOM – through the Pfizer Institute for Pharmaceutical Materials Science), and Unilever for funding FN and JBOM and for supporting the Centre for Molecular Science Informatics.
Status: Peer reviewed
ISSN: 1752-153X
Type: Journal article
Rights: © 2007 O'Boyle et al This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Appears in Collections:University of St Andrews Research
Chemistry Research
Biomedical Sciences Research Complex (BSRC) Research

This item is protected by original copyright

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


DSpace Software Copyright © 2002-2012  Duraspace - Feedback
For help contact: | Copyright for this page belongs to St Andrews University Library | Terms and Conditions (Cookies)