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Ensembling is a successful technique to improve the performance of machinelearning (ML) models.  Conf-Ensemble is an adaptation to Boosting to create ensembles based on modelconfidence instead of model errors to better classify difficult edge-cases. Thekey idea is to create successive model experts for samples that were difficult(not necessarily incorrectly classified) by the preceding model. This techniquehas been shown to provide better results than boosting in binary-classificationwith a small feature space (~80 features).  In this paper, we evaluate the Conf-Ensemble approach in the much morecomplex task of image classification with the ImageNet dataset (224x224x3features with 1000 classes). Image classification is an important benchmark forAI-based perception and thus it helps to assess if this method can be used insafety-critical applications using ML ensembles.  Our experiments indicate that in a complex multi-label classification task,the expected benefit of specialization on complex input samples cannot beachieved with a small sample set, i.e., a good classifier seems to rely on verycomplex feature analysis that cannot be well trained on just a limited subsetof "difficult samples".  We propose an improvement to Conf-Ensemble to increase the number of samplesfed to successive ensemble members, and a three-member Conf-Ensemble using thisimprovement was able to surpass a single model in accuracy, although the amountis not significant. Our findings shed light on the limits of the approach andthe non-triviality of harnessing big data.

Evaluation of Confidence-based Ensembling in Deep Learning Image Classification