Inductive machine learning for instrument development

Abstract An inductive machine learning (ML) algorithm is used to discriminate between respondents and examine the dimensionality of an end-user computing satisfaction instrument. In all 616 responses were partitioned for training and testing purposes. Each respondent was required to assess his or her overall satisfaction level; this was used for classification of responses in two groups: satisfied and dissatisfied. Using 12 other survey items, the Cover Learning using Integer Linear Programming (CLILP2) algorithm correctly categorized 76% of the respondents. Recognition and discrepancy rates were used for instrument validation and in developing a shorter instrument.

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