title: Feature selection for chemical sensor arrays using mutual information.
abstract: We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays.
authors.affiliation: CSIRO Computational Informatics, Epping, NSW, Australia.
authors.forename: X Rosalind
authors.initials: XR
authors.lastname: Wang
authors.affiliation: CSIRO Computational Informatics, Epping, NSW, Australia.
authors.forename: Joseph T
authors.initials: JT
authors.lastname: Lizier
authors.affiliation: CCNR, School of Engineering and Informatics, University of Sussex, Falmer, Brighton United Kingdom; CSIRO Ecosystem Sciences and Food Futures Flagship, Canberra, ACT, Australia.
authors.forename: Thomas
authors.initials: T
authors.lastname: Nowotny
authors.affiliation: CSIRO Ecosystem Sciences and Food Futures Flagship, Canberra, ACT, Australia.
authors.forename: Amalia Z
authors.initials: AZ
authors.lastname: Berna
authors.affiliation: CSIRO Computational Informatics, Epping, NSW, Australia.
authors.forename: Mikhail
authors.initials: M
authors.lastname: Prokopenko
authors.affiliation: CSIRO Ecosystem Sciences and Food Futures Flagship, Canberra, ACT, Australia.
authors.forename: Stephen C
authors.initials: SC
authors.lastname: Trowell
mesh_terms: D000465:Algorithms; D062126:Databases, Chemical; D062609:Electronic Nose; D007256:Information Systems; D015203:Reproducibility of Results; D060388:Support Vector Machine