Non-Linear Decision Trees - NDT
نویسندگان
چکیده
Most decision tree algorithms focus on uni variate i e axis parallel tests at each inter nal node of a tree Oblique decision trees use multivariate linear tests at each non leaf node This paper reports a novel approach to the construction of non linear decision trees The crux of this method consists of the gen eration of new features and the augmenta tion of the primitive features with these new ones The resulted non linear decision trees are more accurate than their axis parallel or oblique counterparts Experiments on sev eral arti cial and real world data sets demon strate this property
منابع مشابه
Discovery of Relevant New Features by Generating Non-Linear Decision Trees
field of manufacturing new features. Most decision tree algorithms using selective induction focus on univariate, i.e. axis-parallel tests at each internal node of a tree. Oblique decision trees use multivariate linear tests at each non-leaf node. One well-known limitation of selective induction algorithms, however, is its inadequate description of hypotheses by task-supplied original features....
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