@inproceedings{792dbfa6daed416197932ce0ad3b2f06,
title = "An empirical study on several classification algorithms and their improvements",
abstract = "Classification algorithms as an important technology in data mining and machine learning have been widely studied and applied. Many methods can be used to build classifiers, such as the decision tree, Bayesian method, instance-based learning, artificial neural network and support vector machine. This paper focuses on the classification methods based on decision tree learning, Bayesian learning, and instance-based learning. In each kind of classification methods, many improvements have been presented to scale up the classification accuracy of the basic algorithm. The paper also studies and compares the classification performance on classification accuracy empirically, using the whole 36 UCI data sets obtained from various sources selected by Weka. The experiment results re-demonstrate the efficiency of all these improved algorithms.",
keywords = "Classification, Decision tree learning, Bayesian learning, Instance-based learning, Empirical study, Classification accuracy",
author = "Jia Wu and Zhechao Gao and Chenxi Hu",
year = "2009",
doi = "10.1007/978-3-642-04843-2_30",
language = "English",
isbn = "9783642048425",
series = "Lecture Notes in Computer Science",
publisher = "Springer, Springer Nature",
pages = "276--286",
editor = "Zhihua Cai and Zhenhua Li and Zhuo Kang and Yong Liu",
booktitle = "Advances in Computation and Intelligence",
address = "United States",
note = "4th International Symposium on Intelligence Computation and Applications (ISICA 2009) ; Conference date: 23-10-2009 Through 25-10-2009",
}