An empirical study on several classification algorithms and their improvements

Jia Wu, Zhechao Gao, Chenxi Hu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationAdvances in Computation and Intelligence
Subtitle of host publication4th International Symposium, ISICA 2009, Proceedings
EditorsZhihua Cai, Zhenhua Li, Zhuo Kang, Yong Liu
PublisherSpringer, Springer Nature
Pages276-286
Number of pages11
ISBN (Electronic)9783642048432
ISBN (Print)9783642048425
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event4th International Symposium on Intelligence Computation and Applications (ISICA 2009) - Huangshi, China
Duration: 23 Oct 200925 Oct 2009

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume5821
ISSN (Print)0302-9743

Conference

Conference4th International Symposium on Intelligence Computation and Applications (ISICA 2009)
CountryChina
CityHuangshi
Period23/10/0925/10/09

Keywords

  • Classification
  • Decision tree learning
  • Bayesian learning
  • Instance-based learning
  • Empirical study
  • Classification accuracy

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