Article

Improved C4.5 algorithm for rule based classification

Authors:

Mohammed M. Mazid,

A. B. M. Shawkat Ali,

Kevin S. TickleAuthors Info & Claims

AIKED'10: Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases

Pages 296 - 301

Published: 20 February 2010 Publication History

Abstract

C4.5 is one of the most popular algorithms for rule base classification. There are many empirical features in this algorithm such as continuous number categorization, missing value handling, etc. However in many cases it takes more processing time and provides less accuracy rate for correctly classified instances. On the other hand, a large dataset might contain hundreds of attributes. We need to choose most related attributes among them to perform higher accuracy using C4.5. It is also a difficult task to choose a proper algorithm to perform efficient and perfect classification. With our proposed method, we select the most relevant attributes from a dataset by reducing input space and simultaneously improve the performance of this algorithm. The improved performance is measured based on better accuracy and less computational complexity. We measure Entropy of Information Theory to identify the central attribute for a dataset. Then apply correlation coefficient measure namely, Pearson's, Spearman, Kendall correlation utilizing the central attribute of the same dataset. We conduct a comparative study using these three most popular correlation coefficient measures to choose the best method on eight well known data mining problem from UCI (University of California Irvine) data repository. We use box plot to compare experimental results. Our proposed method shows better performance in most of the individual experiment.

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Cited By

Venkatachalapathy KKamaleshwar TSundaranarayana DPrakash VAkila VSivakumar NSaruladha KZayaraz GIlavarasan E(2016)An Effective Framework with N-Client Transfer Dataset for Weather Prediction Using Data Mining TechniquesProceedings of the International Conference on Informatics and Analytics10.1145/2980258.2982116(1-6)Online publication date: 25-Aug-2016
https://dl.acm.org/doi/10.1145/2980258.2982116
Ruiz-Fernández DMonsalve Torra ASoriano-Payá AMarín-Alonso OTriana Palencia E(2016)Aid decision algorithms to estimate the risk in congenital heart surgeryComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2015.12.021126:C(118-127)Online publication date: 1-Apr-2016
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Chung HChen RHan SKang B(2016)Combining RDR-based machine learning approach and human expert knowledge for phishing predictionProceedings of the 14th Pacific Rim International Conference on Trends in Artificial Intelligence10.1007/978-3-319-42911-3_7(80-92)Online publication date: 22-Aug-2016
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Index Terms

Improved C4.5 algorithm for rule based classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Guide Proceedings

AIKED'10: Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases

February 2010

394 pages

ISBN:9789604741540

Editors:
Lotfi A. Zadeh
University of Berkeley
,
Janusz Kacprzyk
International Fuzzy Systems Association, Poland
,
Nikos Mastorakis
Technical University of Sofia, Bulgaria
,
Angel Kuri-Morales
Instituto Tecnologico Autonomo de Mexico, Mexico
,
Pierre Borne
Ecole Centrale de Lille, France
,
Leonid Kazovsky
Stanford University

Publisher

World Scientific and Engineering Academy and Society (WSEAS)

Stevens Point, Wisconsin, United States

Publication History

Published: 20 February 2010

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Cited By

Venkatachalapathy KKamaleshwar TSundaranarayana DPrakash VAkila VSivakumar NSaruladha KZayaraz GIlavarasan E(2016)An Effective Framework with N-Client Transfer Dataset for Weather Prediction Using Data Mining TechniquesProceedings of the International Conference on Informatics and Analytics10.1145/2980258.2982116(1-6)Online publication date: 25-Aug-2016
https://dl.acm.org/doi/10.1145/2980258.2982116
Ruiz-Fernández DMonsalve Torra ASoriano-Payá AMarín-Alonso OTriana Palencia E(2016)Aid decision algorithms to estimate the risk in congenital heart surgeryComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2015.12.021126:C(118-127)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1016/j.cmpb.2015.12.021
Chung HChen RHan SKang B(2016)Combining RDR-based machine learning approach and human expert knowledge for phishing predictionProceedings of the 14th Pacific Rim International Conference on Trends in Artificial Intelligence10.1007/978-3-319-42911-3_7(80-92)Online publication date: 22-Aug-2016
https://dl.acm.org/doi/10.1007/978-3-319-42911-3_7
Patidar AAgarwal VBharadwaj K(2012)Predicting Friends and Foes in Signed Networks Using Inductive Inference and Social Balance TheoryProceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)10.1109/ASONAM.2012.69(384-388)Online publication date: 26-Aug-2012
https://dl.acm.org/doi/10.1109/ASONAM.2012.69

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