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A reflected feature space for CART
Author(s) -
Wickramarachchi D. C.,
Robertson B. L.,
Reale M.,
Price C. J.,
Brown J. A.
Publication year - 2019
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/anzs.12275
Subject(s) - oblique case , mathematics , decision tree , feature (linguistics) , feature vector , algorithm , node (physics) , decision tree learning , space (punctuation) , classifier (uml) , tree (set theory) , artificial intelligence , pattern recognition (psychology) , computer science , combinatorics , philosophy , linguistics , structural engineering , engineering , operating system
Summary We present an algorithm for learning oblique decision trees, called HHCART(G). Our decision tree combines learning concepts from two classification trees, HHCART and Geometric Decision Tree (GDT). HHCART(G) is a simplified HHCART algorithm that uses linear structure in the training examples, captured by a modified GDT angle bisector, to define splitting directions. At each node, we reflect the training examples with respect to the modified angle bisector to align this linear structure with the coordinate axes. Searching axis parallel splits in this reflected feature space provides an efficient and effective way of finding oblique splits in the original feature space. Our method is much simpler than HHCART because it only considers one reflected feature space for node splitting. HHCART considers multiple reflected feature spaces for node splitting making it more computationally intensive to build. Experimental results show that HHCART(G) is an effective classifier, producing compact trees with similar or better results than several other decision trees, including GDT and HHCART trees.