Abstract
Interactive Genetic Algorithms (IGAs) are subjective and interactive methods to evaluate the qualities of offspring generated by genetic operations. We adopt IGA in order to select relevant features in inductive learning problems. The method we propose is used to extract efficient decision knowledge from noisy questionnaire data in marketing data analysis domain. Unlike popular learning-from-example methods, in such tasks, we must interpret the characteristics of the data without clear features of the data nor pre-determined evaluation criteria. The problem is how domain experts get simple, easy-tounderstand, and accurate knowledge from noisy data. The proposed method has been qualitatively and quantitatively validated by a case study on consumer product questionnaire data: the acquired rules are simpler than the results from the direct application of inductive learning; a domain expert admits that they are easy to understand; and they are at the same level on the accuracy compared with the other statistical methods. 1
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