A reflected feature space for CART. (28th October 2019)

• Record Type: Journal Article
• Authors: Wickramarachchi, D. C.; Robertson, B. L.; Reale, M.; Price, C. J.; Brown, J. A.
• Is Part Of: Australian & New Zealand journal of statistics. Volume 61:Number 3(2019)
• Page Start: 380
• Page End: 391
• Publication Date: 2019-10-28
• Subjects: decision trees -- geometric decision tree -- multi‐class classification -- oblique decision tree; Statistics -- Periodicals; 519.5
• DOI: 10.1111/anzs.12275
• Languages: English
• ISSNs: 1369-1473

Abstract

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. Abstract : We present a new algorithm for learning oblique decision trees, called HHCART(G). Experimental results show that HHCART(G) is an effective classifier.