Classification by Voting Feature Intervals
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.
Cite
Text
Demiröz and Güvenir. "Classification by Voting Feature Intervals." European Conference on Machine Learning, 1997. doi:10.1007/3-540-62858-4_74Markdown
[Demiröz and Güvenir. "Classification by Voting Feature Intervals." European Conference on Machine Learning, 1997.](https://mlanthology.org/ecmlpkdd/1997/demiroz1997ecml-classification/) doi:10.1007/3-540-62858-4_74BibTeX
@inproceedings{demiroz1997ecml-classification,
title = {{Classification by Voting Feature Intervals}},
author = {Demiröz, Gülsen and Güvenir, H. Altay},
booktitle = {European Conference on Machine Learning},
year = {1997},
pages = {85-92},
doi = {10.1007/3-540-62858-4_74},
url = {https://mlanthology.org/ecmlpkdd/1997/demiroz1997ecml-classification/}
}