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A Novel Multiway Splits Decision Tree for Multiple Types of Data
Joint Authors
Zhang, Qilong
Liu, Zhenyu
Wen, Tao
Sun, Wei
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-16
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Classical decision trees such as C4.5 and CART partition the feature space using axis-parallel splits.
Oblique decision trees use the oblique splits based on linear combinations of features to potentially simplify the boundary structure.
Although oblique decision trees have higher generalization accuracy, most oblique split methods are not directly conducive to the categorical data and are computationally expensive.
In this paper, we propose a multiway splits decision tree (MSDT) algorithm, which adopts feature weighting and clustering.
This method can combine multiple numerical features, multiple categorical features, or multiple mixed features.
Experimental results show that MSDT has excellent performance for multiple types of data.
American Psychological Association (APA)
Liu, Zhenyu& Wen, Tao& Sun, Wei& Zhang, Qilong. 2020. A Novel Multiway Splits Decision Tree for Multiple Types of Data. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1200742
Modern Language Association (MLA)
Liu, Zhenyu…[et al.]. A Novel Multiway Splits Decision Tree for Multiple Types of Data. Mathematical Problems in Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1200742
American Medical Association (AMA)
Liu, Zhenyu& Wen, Tao& Sun, Wei& Zhang, Qilong. A Novel Multiway Splits Decision Tree for Multiple Types of Data. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1200742
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1200742