Searching for Splitting Criteria in Multivariate Decision Tree Using Adapted JADE Optimization Algorithm

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Publication Details

Author list: Jariyavajee C., Polvichai J., Sirinaovakul B.

Publisher: Hindawi

Publication year: 2019

Start page: 2534

End page: 2540

Number of pages: 7

ISBN: 9781728124858

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080905472&doi=10.1109%2fSSCI44817.2019.9003063&partnerID=40&md5=6c1564d1b072f5fbad96f2c990c7a547

Languages: English-Great Britain (EN-GB)

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Abstract

Splitting criteria in the decision tree are normally univariate and orthogonal to the parameter axis. This limitation suppresses the decision power of DT and can be overcome by using multivariate splitting criteria. This paper presents an improvement of splitting criteria searching in a decision tree based on swarm intelligence, the global optimization technique. JADE, one of swarm intelligent algorithms, is adapted in this work to search for optimal parameters for the multivariate linear splitting criteria in the multivariate decision tree building. This paper illustrates the splitting criteria in 2D data space and tests the efficiency of the created decision tree. The experimental results show that AJADE-MDT, the proposed algorithm could be used to create the decision tree with higher efficiency compared to the existing algorithms. ฉ 2019 IEEE.

Keywords

JADE, multivariate decision tree, splitting criteria