Computer system and process for training of analytical models using large data sets

Abstract

A database often contains sparse, i.e., under-represented, conditions which might be not represented in a training data set for training an analytical model if the training data set is created by stratified sampling. Sparse conditions may be represented in a training set by using a data set which includes essentially all of the data in a database, without stratified sampling. A series of samples, or “windows,” are used to select portions of the large data set for phases of training. In general, the first window of data should be a reasonably broad sample of the data. After the model is initially trained using a first window of data, subsequent windows are used to retrain the model. For some model types, the model is modified in order to provide it with some retention of training obtained using previous windows of data. Neural networks and Kohonen networks may be used without modification. Models such as probabilistic neural networks, generalized regression neural networks, Gaussian radial basis functions, decision trees, including K-D trees and neural trees, are modified to provide them with properties of memory to retain the effects of training with previous training data sets. Such a modification may be provided using clustering. is Parallel training models which partition the training data set into disjoint subsets are modified so that the partitioner is trained only on the first window of data, whereas subsequent windows are used to train the models to which the partitioner applies the data in parallel.