Improving the Random Forest Algorithm by Randomly Varying the Size of the Bootstrap Samples for Low Dimensional Data Sets

Md Nasim Adnan, Md Zahidul Islam

Research output: Book chapter/Published conference paperConference paper

4 Citations (Scopus)
17 Downloads (Pure)

Abstract

The Random Forest algorithm generates quite diverse decision trees as the base classifiers for high dimensional data sets. However, for low dimensional data sets the diversity among the trees falls sharply. In Random Forest, the size of the bootstrap samples generally remains the same every time to generate a decision tree as the base classifier. In this paper we propose to vary the size of the bootstrap samples randomly within a predefined range in order to increase diversity among the trees. We conduct an elaborate experimentation on several low dimensional data sets from UCI Machine Learning Repository. The experimental results show the effectiveness of our proposed technique.
Original languageEnglish
Title of host publicationESANN 2015 proceedings
Subtitle of host publicationEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Place of PublicationBelgium
Publisheri6doc.com publication
Pages391-396
Number of pages6
ISBN (Print)9782875870148
Publication statusPublished - 2015
EventThe 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Novotel Hotel, Bruges, Belgium
Duration: 22 Apr 201524 Apr 2015
https://www.elen.ucl.ac.be/esann/

Conference

ConferenceThe 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
CountryBelgium
CityBruges
Period22/04/1524/04/15
OtherThis event builds upon a very successful series of conference organized each year since 1993. ESANN has become a major scientific events in the machine learning, computational intelligence and artificial neural networks fields over the years. The two main tracks are "Theory and methods", and "Information processing and applications". See call for papers for details. In addition, a number of special sessions will be organized, on selected hot topics in the machine learning, computational intelligence and artificial neural networks fields. See special sessions for details.
Internet address

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  • Cite this

    Adnan, M. N., & Islam, M. Z. (2015). Improving the Random Forest Algorithm by Randomly Varying the Size of the Bootstrap Samples for Low Dimensional Data Sets. In ESANN 2015 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 391-396). i6doc.com publication.