Evolutionary Feature Selection for Classification: A Plug-in Hybrid Vehicle Adoption Application

摘要

We present a real-world application utilizing a Genetic Algorithm (GA) for exploratory multivariate association analysis of a large consumer survey designed to assess potential consumer adoption of Plug-in Hybrid Electric Vehicles (PHEVs). The GA utilizes an intersection/union crossover operator, in conjunction with high background mutation rates, to achieve rapid multivariate feature selection. We experimented with two alternative fitness measures based on classification results of a naieve Bayes quadratic discriminant analysis; one fitness function rewarded only for correct classifications, and the other penalized for the degree of misclassification using a quadratic penalty function. We achieved high classification accuracy for three different survey outcome questions (with 3-, 5-, and 7- outcome classes, respectively). The quadratic penalty function yielded better overall results, returning smaller feature sets and overall more accurate contingency tables of predicted classes. Our results help to identify what consumer attributes best predict their likelihood of purchasing a PHEV. These findings will be used to better inform an existing agent-based model of PHEV market penetration, with the ultimate aim of helping auto manufacturers and policy makers identify leverage points in the system that will encourage PHEV market adoption.