Feature segmentation-based ensemble learning for classification and regression

Abstract

Constructing a feature segment-based ensemble can include generating a data structure for each element of an initial set of training data. Multiple strongly correlated features of the elements can be identified as well as weakly correlated features. For each strongly correlated feature, a feature segmentation training set can be generated, each training set's elements each containing one of the strongly correlated features and excluding other strongly correlated features. One or more machine learning algorithms can be selected from a software library. The one or more machine learning algorithms can be applied to the feature segmentation training sets to train multiple machine learning models. Each machine learning model that improves the predictive accuracy of the feature segment-based ensemble can be integrated in the feature segment-based ensemble.