Maximizing benefit of classifications using feature intervals
Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
2773 PART 1
339 - 345
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Please cite this item using this persistent URLhttp://hdl.handle.net/11693/27510
There is a great need for classification methods that can properly handle asymmetric cost and benefit constraints of classifications. In this study, we aim to emphasize the importance of classification benefits by means of a new classification algorithm, Benefit-Maximizing classifier with Feature Intervals (BMFI) that uses feature projection based knowledge representation. Empirical results show that BMFI has promising performance compared to recent cost-sensitive algorithms in terms of the benefit gained.
- Conference Paper 2294
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