A Recursive Partitioning Rule for Binary Decision Trees

A Recursive Partitioning Decision Rule for Nonparametric Lassification *

A new criterion for driving a recursive partitioning decision rule for nonparametric classification is presented. The criterion is both conceptually and computationally simple, and can be shown to have strong statistical merit. The resulting decision rule is asymptotically Bayes risk efficient. The notion of adaptively generated features is introduced and methods are presented for dealing with ...

Binary Space Partitioning (BSP) Trees

A binary space partitioning (bsp) tree represents a recursive, hierarchical subdivision of d-dimensional space into convex subspaces. BSP trees provide a computational representation of space that provides both a search structure and a representation of geometry. Constructing a bsp tree consists of partitioning a subspace by means of a hyperplane, which intersects the interior of that subspace,...

A Tutorial on Binary Space Partitioning Trees

Binary space partitioning trees: a multiresolution approach

Space partitioning techniques are a useful means of organizing geometric models into data structures. Such data structures provide easy and efficient access to a wide range of computer graphics and visualization applications like real-time rendering of large data bases, collision detection, point classification, etc. Binary Space Partitioning (BSP) trees are one of the most successful space par...

Rule Generation using Decision Trees

A DT is a classification scheme which generates a tree and a set of rules, representing the model of different classes, from a given dataset. As per Hans and Kamber [HK01], DT is a flow chart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test and leaf nodes represent the classes or class distributions. The top most node in...