Demiröz, GülşenGüvenir, H. Altay2016-02-082016-02-081997-040302-9743http://hdl.handle.net/11693/27715Date of Conference: 23 - 25 April, 1997Conference name: ECML '97 Proceedings of the 9th European Conference on Machine LearningA new classification algorithm called VFI (for Voting Feature Intervals) is proposed. A concept is represented by a set of feature intervals on each feature dimension separately. Each feature participates in the classification by distributing real-valued votes among classes. The class receiving the highest vote is declared to be the predicted class. VFI is compared with the Naive Bayesian Classifier, which also considers each feature separately. Experiments on real-world datasets show that VFI achieves comparably and even better than NBC in terms of classification accuracy. Moreover, VFI is faster than NBC on all datasets. © Springer-Verlag Berlin Heidelberg 1997.EnglishAlgorithmsArtificial intelligenceLearning systemsClassification accuracyClassification algorithmFeature dimensionsNaive Bayesian ClassifierReal-world datasetsClassification (of information)Conference Paper10.1007/3-540-62858-4_74