Ensembling is a successful technique to improve the performance of machine
learning (ML) models.
Conf-Ensemble is an adaptation to Boosting to create ensembles based on model
confidence instead of model errors to better classify difficult edge-cases. The
key idea is to create successive model experts for samples that were difficult
(not necessarily incorrectly classified) by the preceding model. This technique
has been shown to provide better results than boosting in binary-classification
with a small feature space (~80 features).
In this paper, we evaluate the Conf-Ensemble approach in the much more
complex task of image classification with the ImageNet dataset (224x224x3
features with 1000 classes). Image classification is an important benchmark for
AI-based perception and thus it helps to assess if this method can be used in
safety-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 be
achieved with a small sample set, i.e., a good classifier seems to rely on very
complex feature analysis that cannot be well trained on just a limited subset
of "difficult samples".
We propose an improvement to Conf-Ensemble to increase the number of samples
fed to successive ensemble members, and a three-member Conf-Ensemble using this
improvement was able to surpass a single model in accuracy, although the amount
is not significant. Our findings shed light on the limits of the approach and
the non-triviality of harnessing big data.

使用了 Conf-Ensemble 技术来提高机器学习模型的性能，通过对 ImageNet 数据集的图像分类实验，发现 Conf-Ensemble 在复杂的多标签分类任务中并未能如预期一样实现优化结果，也说明了大数据难以被利用的非平凡性。