Classification by voting feature intervals

Date
1997-04
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Source Title
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Print ISSN
0302-9743
Electronic ISSN
Publisher
Springer
Volume
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Pages
85 - 92
Language
English
Type
Conference Paper
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Abstract

A 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.

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Keywords
Algorithms, Artificial intelligence, Learning systems, Classification accuracy, Classification algorithm, Feature dimensions, Naive Bayesian Classifier, Real-world datasets, Classification (of information)
Citation
Published Version (Please cite this version)