Communications of the ACM - Special issue on parallelism
Boolean Feature Discovery in Empirical Learning
Machine Learning
Incremental, instance-based learning of independent and graded concept descriptions
Proceedings of the sixth international workshop on Machine learning
Instance-Based Learning Algorithms
Machine Learning
A Nearest Hyperrectangle Learning Method
Machine Learning
Trading MIPS and memory for knowledge engineering
Communications of the ACM
Generalizing from case studies: a case study
ML92 Proceedings of the ninth international workshop on Machine learning
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Case-based reasoning
An instance-based learning method for databases: an information theoretic approach
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Unifying instance-based and rule-based induction
Machine Learning
Artificial Intelligence Review - Special issue on lazy learning
Machine Learning
Learning a Local Similarity Metric for Case-Based Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Artificial Intelligence Review - Special issue on lazy learning
Conversational Case-Based Reasoning
Applied Intelligence
Deleting and Building Sort Out Techniques for Case Base Maintenance
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Rough Sets Reduction Techniques for Case-Based Reasoning
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Local Feature Selection with Dynamic Integration of Classifiers
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Feature selection based on a modified fuzzy C-means algorithm with supervision
Information Sciences—Informatics and Computer Science: An International Journal
Advanced Local Feature Selection in Medical Diagnostics
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Weighting Features to Recognize 3D Patterns of Electron Density in X-Ray Protein Crystallography
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting
Applied Intelligence
Randomized Variable Elimination
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Instance based learning with automatic feature selection applied to word sense disambiguation
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Local sparsity control for naive Bayes with extreme misclassification costs
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Local Feature Selection with Dynamic Integration of Classifiers
Fundamenta Informaticae - Intelligent Systems
Expert Systems with Applications: An International Journal
A decision tree-based attribute weighting filter for naive Bayes
Knowledge-Based Systems
Local distance-based classification
Knowledge-Based Systems
Global optimization of case-based reasoning for breast cytology diagnosis
Expert Systems with Applications: An International Journal
Improving similarity assessment with entropy-based local weighting
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
A decision support system for cost-effective diagnosis
Artificial Intelligence in Medicine
Unsupervised case memory organization: analysing computational time and soft computing capabilities
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Local Feature Selection with Dynamic Integration of Classifiers
Fundamenta Informaticae - Intelligent Systems
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High sensitivity to irrelevant features is arguably the mainshortcoming of simple lazy learners. In response to it, many featureselection methods have been proposed, including forward sequentialselection (FSS) and backward sequential selection (BSS). Althoughthey often produce substantial improvements in accuracy, these methodsselect the same set of relevant features everywhere in the instancespace, and thus represent only a partial solution to the problem. Ingeneral, some features will be relevant only in some parts of thespace; deleting them may hurt accuracy in those parts, but selectingthem will have the same effect in parts where they are irrelevant.This article introduces RC, a new feature selection algorithm thatuses a clustering-like approach to select sets of locally relevantfeatures (i.e., the features it selects may vary from one instance toanother). Experiments in a large number of domains from the UCIrepository show that RC almost always improves accuracy with respectto FSS and BSS, often with high significance. A study using artificialdomains confirms the hypothesis that this difference in performance isdue to RC‘s context sensitivity, and also suggests conditions wherethis sensitivity will and will not be an advantage. Another featureof RC is that it is faster than FSS and BSS, often by an order ofmagnitude or more.