A cognitive bias approach to feature selection and weighting for case-based learners

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. We first show that cognitive biases can be automatically and explicitly encoded into the baseline instance representation: each bias modifies the representation by changing features, deleting features, or modifying feature weights. Next, we investigate the related problems of cognitive bias selection and cognitive bias interaction for the feature weighting approach. In particular, we compare two cross-validation algorithms for bias selection that make different assumptions about the independence of individual component biases. In evaluations on four natural language learning tasks, we show that the bias selection algorithms can determine which cognitive bias or biases are relevant for each learning task and that the accuracy of the case-based learning algorithm improves significantly when the selected bias(es) are incorporated into the baseline instance representation.