1 Citation (Scopus)

Abstract

Due to its simplicity and good performance, Random Forest attains much interest from the research community. The splitting attribute at each node of a decision tree for Random Forest is determined from a predefined number of randomly selected attributes (a subset of the entire attribute set). The size of an attribute subset (subspace) is one of the most important factors that stems multitude of influences over Random Forest. In this paper, we propose a new technique that dynamically determines the size of subspaces based on the relative size of the current data segment to the entire data set. In order to assess the effects of the proposed technique, we conduct experiments involving five widely used data set from the UCI Machine Learning Repository. The experimental results indicate the capability of the proposed technique on improving the ensemble accuracy of Random Forest.
Original languageEnglish
Title of host publicationProceedings of the 13th International Conference of Advanced Data Mining and Applications (ADMA 2017)
EditorsGao Cong, Wen-Chih Peng, Wei Emma Zhang, Chengliang Li, Aixin Sun
PublisherSpringer
Pages303-312
Number of pages10
ISBN (Electronic) 9783319691794
ISBN (Print)9783319691787
DOIs
Publication statusPublished - 2017
Event13th International Conference on Advanced Data Mining and Applications: ADMA 2017 - Nanyang Technological University Alumni House, Singapore, Singapore
Duration: 05 Nov 201706 Nov 2017
https://web.archive.org/web/20170826073420/http://www.adma2017.net/#0 (Conference website)
https://www.springer.com/gp/book/9783319691787 (Conference proceedings)

Publication series

NameEdit Lecture Notes in Artificial Intelligence
PublisherSpringer
Volume10604
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Advanced Data Mining and Applications
CountrySingapore
CitySingapore
Period05/11/1706/11/17
Internet address

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