A Comparison of Classification Algorithms Based on the Number of Features

Peichen Xiong, Wei Ping

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


As the most widely known data mining algorithm, classification algorithms have attracted wide attention. K-Nearest Neighbor (KNN) algorithm and decision tree algorithm are the two widely known algorithms in classification algorithms. Sometimes, people not sure how to choose the suitable to solve the classification problems. In this paper, we establish KNN algorithm model and decision tree ID3 algorithm model to analyze the accuracy of the two algorithms in the same data set with different number of features. Through the learning curve and cross validation, we find ID3 algorithm is better than KNN algorithm, and when the number of feature increased the accuracy of KNN is increasing while ID3 is decreased.

Original languageEnglish
Title of host publicationProceedings of the 39th Chinese Control Conference, CCC 2020
EditorsJun Fu, Jian Sun
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9789881563903
Publication statusPublished - Jul 2020
Externally publishedYes
Event39th Chinese Control Conference, CCC 2020 - Shenyang, China
Duration: 27 Jul 202029 Jul 2020

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927


Conference39th Chinese Control Conference, CCC 2020


  • Classification Algorithm
  • Decision Tree
  • ID3
  • KNN


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