A reflected feature space for CART (2019)
This paper presents 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 nodes 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.
CitationWickramarachchi DC, Robertson BL, Reale M, Price CJ, Brown JA (2019). A reflected feature space for CART. Australian & New Zealand Journal of Statistics. 61(3), 2019, 380-391.
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Keywordsdecision trees; geometric decision tree; multi-class classification; oblique decision tree
ANZSRC Fields of Research49 - Mathematical sciences::4905 - Statistics::490599 - Statistics not elsewhere classified
08 - Information and Computing Sciences::0801 - Artificial Intelligence and Image Processing
49 - Mathematical sciences::4904 - Pure mathematics::490404 - Combinatorics and discrete mathematics (excl. physical combinatorics)
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