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A New Algorithm for Classification Based on Multi-classifiers Learning

  • Yifeng Zheng
  • Guohe Li
  • Wenjie Zhang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 849)

Abstract

Quality and quantity are the two key factors to influence the accuracy of classification. In order to improve the classification accuracy, in this paper, we propose a new algorithm, called CMCM (Classification based on Multiple Classifier Models), which consists of two classification models. In Model1, we mainly focus on the improvement of quality, thus the best attribute value from both the items and their complements in the training set is selected as the first item of a classification rule. While in Model2, quantity is taken into consideration, so it constructs two candidate sets and uses the one-versus-many strategy to generate several rules at one time. The experiment results show that: (1) Model1 can extract sufficient high quality rules and achieve high classification accuracy. (2) Model2 can extract sufficient information and achieve high classification accuracy. (3) CMCM can achieve higher classification accuracy compare with traditional classification.

Keywords

Data mining Classification Ensemble learning Classification rule 

Notes

Acknowledgments

This work is supported by Natural Science Funds of China (Nos. 61701213), Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, the Program for Excellent Talents of Fujian Province, the Special Research Fund for Higher Education of Fujian (No. JK2015027), and the Research Fund for Educational Department of Fujian Province (No. JA15300).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Computer Sciences, Key Laboratory of Data Science and Intelligence ApplicationFujian Province University, Minnan Normal UniversityZhangzhouChina
  2. 2.College of Geophysics and Information Engineering, Beijing Key Lab of Data Mining for Petroleum Data UniversityChina University of PetroleumBeijingChina

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