• Aug 06, 2018 News! IJIET Vol. 7, No. 1-No. 8 have been indexed by EI (Inspec).   [Click]
  • Mar 06, 2019 News!Vol. 9, No. 4 issue has been published online!   [Click]
  • Feb 25, 2019 News!Vol. 9, No. 3 issue has been published online!   [Click]
General Information
    • ISSN: 2010-3689
    • Frequency: Bimonthly (2011-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJIET
    • Editor-in-Chief: Prof. Dr. Steve Thatcher
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: Scopus (Since 2019), EI(INSPEC, IET), Electronic Journals Library, Google Scholar, Crossref, etc.
    • E-mail: ijiet@ejournal.net
Prof. Dr. Steve Thatcher
QUniversity, Australia
It is my honor to be the editor-in-chief of IJIET. The journal publishes good-quality papers which focous on the advanced researches in the field of information and education technology. Hopefully, IJIET will become a recognized journal among the scholars in the related fields.

IJIET 2013 Vol.3(5): 560-566 ISSN: 2010-3689
DOI: 10.7763/IJIET.2013.V3.335

Weight-Adjusted Bagging of Classification Algorithms Sensitive to Missing Values

Kuo-Wei Hsu
Abstract—Bagging is commonly used to improve the performance of a classification algorithm by first using bootstrap sampling on the given data set to train a number of classifiers and then using the majority voting mechanism to aggregate their outputs. However, the improvement would be limited in the situation where the given data set contains missing values and the algorithm used to train the classifiers is sensitive to missing values. We propose an extension of bagging that considers not only the weights of the classifiers in the voting process but also the incompleteness of the bootstrapped data sets used to train the classifiers. The proposed extension assigns a weight to each of the classifiers according to its classification performance and adjusts the weight of each of the classifiers according to the ratio of missing values in the data set on which it is trained. In experiments, we use two classification algorithms, two measures for weight assignment, and two functions for weight adjustment. The results reveal the potential of the proposed extension of bagging for working with classification algorithms sensitive to missing values to perform classification on data sets having small numbers of instances but containing relatively large numbers of missing values.

Index Terms—Bagging, missing values, multilayer perceptron, sequential minimal optimization.

Kuo-Wei Hsu is with the Department of Computer Science, National Chengchi University, Taipei, Taiwan (e-mail: hsu@cs.nccu.edu.tw).


Cite:Kuo-Wei Hsu, "Weight-Adjusted Bagging of Classification Algorithms Sensitive to Missing Values," International Journal of Information and Education Technology vol. 3, no. 5, pp. 560-566, 2013.

Copyright © 2008-2019. International Journal of Information and Education Technology. All rights reserved.
E-mail: ijiet@ejournal.net