Specific Random Trees for Random Forest

Zhi LIU  Zhaocai SUN  Hongjun WANG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.3   pp.739-741
Publication Date: 2013/03/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.739
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Artificial Intelligence, Data Mining
Keyword: 
random forest,  multiclass classification,  specific random trees,  

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Summary: 
In this study, a novel forest method based on specific random trees (SRT) was proposed for a multiclass classification problem. The proposed SRT was built on one specific class, which decides whether a sample belongs to a certain class. The forest can make a final decision on classification by ensembling all the specific trees. Compared with the original random forest, our method has higher strength, but lower correlation and upper error bound. The experimental results based on 10 different public datasets demonstrated the efficiency of the proposed method.