Multi-dimensional Raycasting for Fuzzy Pattern Classication

One of the most important problems in artificial intelligence, namely pattern classification, is essentially equivalent to finding hyper-surfaces separating the different classes of data in a high-dimensional space. A method called raycasting is commonly used in computer game programming to locate and describe surfaces in a 2-dimesional map. A viewer, situated at some point on this map, casts rays of light towards various directions, and the rays extend until they hit a part of a surface. As a result, the surface can be described in polar coordinates as a set of vectors of varying lengths and angles. In this work we present a pattern classification system loosely based on the raycasting concept. We modify the method to create fuzzy pattern classification rules in 2 dimensions, and then generalize the rules to high-dimensional spaces. The resulting classifier is tested on a number of benchmark tests from the UCI repository.