Güvenir, H. Altay2016-02-082016-02-081998-090302-9743http://hdl.handle.net/11693/27656Date of Conference: 21-23 September, 1998Conference name: 8th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA’98Presence of irrelevant features is a fact of life in many realworld applications of classification learning. Although nearest-neighbor classification algorithms have emerged as a promising approach to machine learning tasks with their high predictive accuracy, they are adversely affected by the presence of such irrelevant features. In this paper, we describe a recently proposed classification algorithm called VFI5, which achieves comparable accuracy to nearest-neighbor classifiers while it is robust with respect to irrelevant features. The paper compares both the nearest-neighbor classifier and the VFI5 algorithms in the presence of irrelevant features on both artificially generated and real-world data sets selected from the UCI repository.EnglishClassification algorithmClassification learningNearest neighbor classifiersNearest-neighborsPredictive accuracyArticle10.1007/BFb0057452