A Cognitive Bias Approach to Feature Selection and Weighting for Case-Based Learners

摘要

Research in psychology, psycholinguistics, and cognitive science has discovered and examined numerous psychological constraints on human information processing. Short term memory limitations, a focus of attention bias, and a preference for the use of temporally recent information are three examples. This paper shows that psychological constraints such as these can be used effectively as domain-independent sources of bias to guide feature set selection and weighting for case-based learning algorithms.