This paper presents a new method of generating oblique decision trees.
Oblique trees have been shown to be useful tools for classification in some problem domains, producing accurate and intuitive solutions. Our method can be incorporated into a
variety of existing decision tree tools and the paper illustrates this with two very distinct tree generators. The key idea is a method of learning "good" oblique vectors
and using the corresponding families of hyperplanes orthogonal to these vectors to separate
regions with distinct dominant classes. Experimental results indicate that the learnt oblique hyperplanes lead to compact and accurate oblique trees. A significant advantage of our method is that it can be easily incorporated into most decision tree packages thereby extending them to generate oblique trees.
By: Vijay S. Iyengar
Published in: RC21487 in 1999
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