  An ensemble of classifiers combines several single classifiers to deliver a
final prediction or classification decision. An increasingly provoking question
is whether such systems can outperform the single best classifier. If so, what
form of an ensemble of classifiers (also known as multiple classifier learning
systems or multiple classifiers) yields the most significant benefits in the
size or diversity of the ensemble itself? Given that the tests used to detect
autism traits are time-consuming and costly, developing a system that will
provide the best outcome and measurement of autism spectrum disorder (ASD) has
never been critical. In this paper, several single and later multiple
classifiers learning systems are evaluated in terms of their ability to predict
and identify factors that influence or contribute to ASD for early screening
purposes. A dataset of behavioural data and robot-enhanced therapy of 3,000
sessions and 300 hours, recorded from 61 children are utilised for this task.
Simulation results show the superior predictive performance of multiple
classifier learning systems (especially those with three classifiers per
ensemble) compared to individual classifiers, with bagging and boosting
achieving excellent results. It also appears that social communication gestures
remain the critical contributing factor to the ASD problem among children.
