About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Conference paper
HOT: Heuristics for oblique trees
Abstract
This paper presents a new method (HOT) 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 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. HOT is simple and easy to incorporate into most decision tree packages, yet its results compare well with much more complex schemes for generating oblique trees.