Artificial bee colony optimisation-based enhanced Mahalanobis Taguchi system for classification. (9th December 2014) - Iquebal, Ashif Sikandar; Pal, Avishek
Mahalanobis Taguchi system (MTS) is a classification technique which is used to group objects into dichotomous or binary categories based on numerical predictor variables. MTS uses threshold values based on Mahalanobis distance to group objects into binary classes. An important step in MTS is feature selection whereby a subset of the original set of predictor variables is selected based on maximisation of signal-to-noise (S/N) ratio. Application of S/N ratio for feature selection in MTS has been criticised for its limitations in handling large number of predictor variables. This research explores the application of artificial bee colony (ABC) optimisation to select features in MTS. The problem of feature selection is formulated as minimisation of total weighted misclassification and solved as a binary integer programme. The optimal subset of features is obtained via the stochastic search mechanism of binary ABC. The methodology is applied for five case studies. Results are compared with those obtained from two other meta-heuristic techniques namely genetic algorithm and particle swarm optimisation.