  In this paper, we consider ensemble classifiers, that is, machine learning
based classifiers that utilize a combination of scoring functions. We provide a
framework for categorizing such classifiers, and we outline several ensemble
techniques, discussing how each fits into our framework. From this general
introduction, we then pivot to the topic of ensemble learning within the
context of malware analysis. We present a brief survey of some of the ensemble
techniques that have been used in malware (and related) research. We conclude
with an extensive set of experiments, where we apply ensemble techniques to a
large and challenging malware dataset. While many of these ensemble techniques
have appeared in the malware literature, previously there has been no way to
directly compare results such as these, as different datasets and different
measures of success are typically used. Our common framework and empirical
results are an effort to bring some sense of order to the chaos that is evident
in the evolving field of ensemble learning -- both within the narrow confines
of the malware analysis problem, and in the larger realm of machine learning in
general.
