A Modeling Approach for Datamining and Predictive Modeling Decision Tree Ensembles

The data-mining classification and predictive modeling algorithms that are based on bootstrapping techniques re-use a source data set, repeatedly, to create a family of predictive and classification models that can be said to render a "holographic" view of the modeled data. The results offer a classification and prediction performance that is superior to single-model approaches. These holographic approaches are applied in an industrial setting that involves text mining warranty claims at a major international car, truck, and heavy equipment manufacturer. This paper explains the methods used, how they work, and how they perform in the text-mining area as applied to warranty claims. Combined text and quantitative data models are developed, tested, and validated in order to address the goal of achieving "better-than-human" classification performance on warranty claims.