A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
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Pattern Recognition Letters - Special issue on pattern recognition in practice VI
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Journal of Intelligent Information Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
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IEEE Transactions on Knowledge and Data Engineering
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Pattern Recognition Letters
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Information Sciences: an International Journal
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ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
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RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
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PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
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DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
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ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
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Computers in Biology and Medicine
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Knowledge-Based Systems
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Applied Intelligence
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This paper investigates feature selection based on rough sets for dimensionality reduction in Case-Based Reasoning classifiers. In order to be useful, Case-Based Reasoning systems should be able to manage imprecise, uncertain and redundant data to retrieve the most relevant information in a potentially overwhelming quantity of data. Rough Set Theory has been shown to be an effective tool for data mining and for uncertainty management. This paper has two central contributions: (1) it develops three strategies for feature selection, and (2) it proposes several measures for estimating attribute relevance based on Rough Set Theory. Although we concentrate on Case-Based Reasoning classifiers, the proposals are general enough to be applicable to a wide range of learning algorithms. We applied these proposals on twenty data sets from the UCI repository and examined the impact of feature selection over classification performance. Our evaluation shows that all three proposals benefit the basic Case-Based Reasoning system. They also present robustness in comparison to well-known feature selection strategies.