Marketing data analysis using inductive learning and genetic algorithms with interactive- and automated-phases

In this paper, to analyze questionnaire data on consumer goods for marketing decision making, we use inductive learning and genetic algorithms with interactive and automated phases. The basic idea of the method is to integrate inductive learning to acquire decision trees or sets of decision rules and genetic algorithms to get the effective features to develop simple, easy-to-understand, and accurate knowledge from noisy data. The unique characteristic of the method is that the offspring (decision trees) are evaluated by both human-in-a-loop phase (simulated breeding) and automated simple GA-based phase. The proposed method has been qualitatively and quantitatively validated by a case study on consumer product questionnaire data of 2400 entries with 16 attributes.