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.
This project is supported by TUBITAK (Scientific and Technical Research Council of Turkey) under Grant EEEAG-153.
[1]
I. Bratko,et al.
Information-based evaluation criterion for classifier's performance
,
2004,
Machine Learning.
[2]
J. Ross Quinlan,et al.
Unknown Attribute Values in Induction
,
1989,
ML.
[3]
David W. Aha,et al.
Weighting Features
,
1995,
ICCBR.
[4]
H. Altay Güvenir,et al.
Classification by Feature Partitioning
,
2004,
Machine Learning.
[5]
Catherine Blake,et al.
UCI Repository of machine learning databases
,
1998
.
[6]
H. Altay Güvenir,et al.
K Nearest Neighbor Classification on Feature Projections
,
1996,
ICML.
[7]
Igor Kononenko,et al.
Inductive and Bayesian learning in medical diagnosis
,
1993,
Appl. Artif. Intell..