Patent ID: 7127466

Claim:
A computer-implemented 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 co-linear (providing an input data set including a plurality of co-linear input variables and an associated decision state); input variables and an associated decision state; wherein an input variable has one or more neighbor input variables; (b) calculating a statistical measure of the significance of each of the input variables to the associated decision state; wherein the statistical measure indicates how related an input variable is with respect to its associated decision state; (c) averaging the statistical measures for each of the input variables to form an averaged statistical measure for each input variable; wherein the averaging for an input variable includes using statistical measures calculated for the input variable's one or more neighbor input variables to generate an averaged statistical measure for the input variable; “wherein the result of averaging step smoothes the input data set a significant peak which eliminates a false identifier;” (variable; wherein the result of averaging step smoothes the input data set a significant peak which eliminates a false identifier;) (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; (f) for each of the two subsets in step (e), repeating steps (b) through (e); wherein the input variables in the input data set have an order; wherein the order is used to determine which of the input variables are neighbors of an input variable.