@inproceedings{56439933da584ca6a9c62ff216ea27fd,
title = "Na{\"i}ve Bayes vs. support vector machine: Resilience to missing data",
abstract = "The na{\"i}ve Bayes and support vector machine are the typical generative and discriminative classification models respectively, which are two popular classification approaches. Few studies have been done comparing their resilience to missing data. This paper provides an experimental comparison of the na{\"i}ve Bayes and support vector machine regarding the resilience to missing data on 24 UCI data sets. The experimental results show that when the missing rate is very small (e.g. 1\%), the resilience of the na{\"i}ve Bayes classifiers to missing data are approximately similar to that of support vector machine classifiers. With the increase of the missing rate, however, the resilience of the na{\"i}ve Bayes classifiers to missing data are slowly decreased and that of support vector machine classifiers to missing data are rapidly decreased. This demonstrates that the na{\"i}ve Bayes classifiers have better resilience to missing data than support vector machine classifiers.",
keywords = "SVM, missing data, resilience, the na{\"i}ve Bayes",
author = "Hongbo Shi and Yaqin Liu",
year = "2011",
doi = "10.1007/978-3-642-23887-1\_86",
language = "英语",
isbn = "9783642238864",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "680--687",
booktitle = "Artificial Intelligence and Computational Intelligence - Third International Conference, AICI 2011, Proceedings",
edition = "PART 2",
note = "3rd International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011 ; Conference date: 24-09-2011 Through 25-09-2011",
}