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10

ECML
2003
Springer

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Experiments with Cost-Sensitive Feature Evaluation

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Experiments with Cost-Sensitive Feature Evaluation

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Many machine learning tasks contain feature evaluation as one of its important components. This work is concerned with attribute estimation in the problems where class distribution is unbalanced or the misclassiﬁcation costs are unequal. We test some common attribute evaluation heuristics and propose their cost-sensitive adaptations. The new measures are tested on problems which can reveal their strengths and weaknesses.

Marko Robnik-Sikonja

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Attribute Estimation | Attribute Evaluation Heuristics | ECML 2003 | Tasks Contain Feature |

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Added	06 Jul 2010
Updated	06 Jul 2010
Type	Conference
Year	2003
Where	ECML
Authors	Marko Robnik-Sikonja

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