This paper describes an approach for combining the classifications or predi
ctions 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 exp
erts that provide information for assigning an object to one of two predete
rmined groups. An advantage of this type of application is that it is capab
le of interacting with the Internet in a relatively seamless way. We examin
e the accuracy and robustness of our technique by comparing the classificat
ion accuracy of our technique,, a maximum entropy-based aggregation techniq
ue, and four classification methods on a real-world, two-group data-set con
cerned with bank failure prediction. The classificaiton methods studied in
this work include Quinlan's C4.5 decision-tree classifier, logistic regress
ion, mahalanobis distance measures, and a neural network classifier. Our mo
del includes a fundamental component (i.e., a transaction manager) that hel
ps improve the general performance of applications that perform network-bas
ed classification. This component is found to provide reliable and secure c
onnections along with ways to direct traffic across the Internet. Our resul
ts suggest three major contributions: (1) a transaction manager increases t
he flexibility of a network-based classifier since it is capable of transac
ting with one or more specific types of prediction expert(s) over the Inter
net; (2) our approach tends to be more accurate than the individual classif
ication methods we examined; and, (3) our approach can outperform a recentl
y introduced statistically based aggregation technique.