This paper describes an approach for combining the classifications or predictions of n local experts into a single composite prediction. We describe a Java-based application that allows a user to select up to n prediction experts that provide information for assigning an object to one of two predetermined groups. An advantage of this type of application is that it is capable of interacting with the Internet in a relatively seamless way. We examine the accuracy and robustness of our technique by comparing the classification accuracy of our technique, a maximum entropy-based aggregation technique, and four classification methods on a real-world, two-group data-set concerned with bank failure prediction. The classification methods studied in this work include Quinlan's C4.5 decision-tree classifier, logistic regression, mahalanobis distance measures, and a neural network classifier. Our model includes a fundamental component (i.e., a transaction manager) that helps improve the general performance of applications that perform network-based classification. This component is found to provide reliable and secure connections along with ways to direct traffic across the Internet. Our results suggest three major contributions: (1) a transaction manager increases the flexibility of a network-based classifier since it is capable of transacting with one or more specific types of prediction expert(s) over the Internet; (2) our approach tends to be more accurate than the individual classification methods we examined; and, (3) our approach can outperform a recently introduced statistically based aggregation technique.
