Fusing the Results of Diverse Algorithms

John F. Elder

Structurally adaptive methods -- from decision trees and polynomial networks, to projection pursuit models, additive networks, and cascade correlation neural networks -- iteratively add components in an attempt to construct models with complexity appropriate to the data. The diverse basis functions and search strategies employed usually lead to a distribution of results (with rankings hard to predict priori) yet, robustly combining the output estimates can achieve a consensus model with properties often superior to the best of the individual models. Additionally, other information learned by some of the modeling techniques can be shared to the benefit of the fused system, including identification of key variables to employ and outlying cases to ignore. This paper describes the fused model developed for a small but challenging classification dataset (where one infers the species of a bat from its chirps) and introduces a robust method of combining the outputs of diverse models.

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