Patent ID: 6973459

Claim:
A method of generating an Adaptive Bayes Network data mining model comprising the steps of: receiving a data table having a plurality of predictor columns and a target column and comprising a plurality of rows of data; constructing a plurality of single-predictor models, comprising the steps of: computing a description length of a baseline model based on unconditional target probabilities among the plurality of rows; determining which predictor columns are correlated to the target column based on minimum description length; computing probabilities of at least two target values of the target column conditioned on at least two predictor values of at least one correlated predictor column; and computing a probability of at least one correlated predictor column conditioned on the at least two target values; ranking each predictor column by ranking each single-predictor model using minimum description length and selecting a best single predictor model; performing feature selection based on a minimum of a specified number of predictors and as a function of a reduction in entropy attributable to the best single predictor model; constructing a Naïve Bayes model using a top-ranked portion of the plurality of predictor columns; comparing a description length of the Naive Bayes model with a description length of a baseline model; replacing the baseline model with the Naïve Bayes model, if the description length of the Naive Bayes model is less than the description length of the baseline model; extending a plurality of single-predictor models in rank order, stepwise, to multi-predictor features; and testing whether each new feature should be included in or should replace a current model state using minimum description length.