Patent ID: 6532467
Filing Date: 2003-03-11
Classification: G06K,G16B,G16H,Y10S

Abstract:
A method of selecting node variables for use in building a binary decision tree, comprising the steps of:(a) providing an input data set including a plurality of input variables and an associated decision state; (b) calculating a statistical measure of the significance of each of the input variables to the associated decision state; (c) averaging the statistical measures for each of the input variables and to form an averaged statistical measure for each input variable, wherein the averaging step further comprises the steps of: providing a neighbor parameter indicating how many nearby input variables to use in calculating the average statistical measure; providing a weight parameter indicating weight to apply to each of the nearby input variables used in calculating the average statistical measure; and calculating the average statistical measure for each input variable according to the following equation: AVGCHI&af;(j)=&Sum;k=-NEIGHBORNUMNEIGHBORNUM&it;WEIGHTS&af;[k+NEIGHBORNUM]*MAX&af;[j+k]&Sum;k=02*NEIGHBORNUM&it;WEIGHTS&af;[k],wherein NEIGHBORNUM is the neighbor parameter, WEIGHTS is an array of weight parameters having length NEIGHBORNUM, and MAX is the statistical measure; (d) selecting the input variable with the largest average statistical measure; and (e) using the selected input variable as a node variable for splitting the input data set into two subsets that are used in building the binary decision tree.