Communications of the ACM - Special issue on parallelism
Instance-Based Learning Algorithms
Machine Learning
Trading MIPS and memory for knowledge engineering
Communications of the ACM
Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Elements of machine learning
Evaluation and Selection of Biases in Machine Learning
Machine Learning - Special issue on bias evaluation and selection
Inductive Policy: The Pragmatics of Bias Selection
Machine Learning - Special issue on bias evaluation and selection
Artificial Intelligence Review - Special issue on lazy learning
The Racing Algorithm: Model Selection for Lazy Learners
Artificial Intelligence Review - Special issue on lazy learning
Control-Sensitive Feature Selection for Lazy Learners
Artificial Intelligence Review - Special issue on lazy learning
Artificial Intelligence Review - Special issue on lazy learning
Forgetting Exceptions is Harmful in Language Learning
Machine Learning - Special issue on natural language learning
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Improving Minority Class Prediction Using Case-Specific Feature Weights
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Examining Locally Varying Weights for Nearest Neighbor Algorithms
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
University of Massachusetts: description of the CIRCUS system as used for MUC-3
MUC3 '91 Proceedings of the 3rd conference on Message understanding
Combining Feature Selection with Feature Weighting for k-NN Classifier
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
A Statistical Approach for Binary Vectors Modeling and Clustering
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
On multivariate binary data clustering and feature weighting
Computational Statistics & Data Analysis
Journal of Visual Communication and Image Representation
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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.