Integrating linear discriminant analysis,polynomial basis expansion,and genetic search for two-group classification |
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Authors: | Michael J Brusco Clay M Voorhees Roger J Calantone Michael K Brady Douglas Steinley |
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Institution: | 1. Department of Business Analytics, Information Systems, and Supply Chain, Florida State University, Tallahassee, FL USA;2. Department of Marketing, Michigan State University, East Lansing, Michigan USA;3. Department of Marketing, Florida State University, Tallahassee, Florida, USA;4. Department of Psychological Sciences, University of Missouri-Columbia, Columbia, Missouri USA |
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Abstract: | We propose a hybrid two-group classification method that integrates linear discriminant analysis, a polynomial expansion of the basis (or variable space), and a genetic algorithm with multiple crossover operations to select variables from the expanded basis. Using new product launch data from the biochemical industry, we found that the proposed algorithm offers mean percentage decreases in the misclassification error rate of 50%, 56%, 59%, 77%, and 78% in comparison to a support vector machine, artificial neural network, quadratic discriminant analysis, linear discriminant analysis, and logistic regression, respectively. These improvements correspond to annual cost savings of $4.40–$25.73 million. |
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Keywords: | genetic algorithm linear discriminant analysis two-group classification |
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