Over the last two decades, the machine learning and related communities have conducted
numerous studies to improve the performance of a single classifier by combining several …

This article presents a comprehensive review of stacking methods commonly used to
address the challenge of automatic semantic similarity measurement in the literature. Since …

Slope failures lead to catastrophic consequences in numerous countries and thus the
stability assessment for slopes is of high interest in geotechnical and geological engineering …

机器学习的求解过程可以看作是在假设空间中搜索一个具有强泛化能力和高鲁棒性的学习模型,
而在假设空间中寻找合适模型的过程是较为困难的. 然而, 集成学习作为一类组合优化的学习 …

An ensemble of models is a set of learning models whose individual predictions are
combined in such a way that component models compensate for each other's weaknesses …

Stacking and bagging are widely used ensemble learning approaches that make use of
multiple classifier systems. Stacking focuses on building an ensemble of heterogeneous …

This paper, with an aim at improving neural networks' generalization performance, proposes
an effective neural network ensemble approach with two novel ideas. One is to apply neural …

The importance of industry-specific characteristics in financial distress is widely
acknowledged, but often overlooked by researchers studying the hospitality industry. The …

The use of an accelerometer is considered as a promising method for the automatic
measurement of the feeding behavior or feed intake of cattle, with great significance in …

A Classifier Ensemble combines a finite number of classifiers of same kind or different,
trained simultaneously for a common classification task. The Ensemble efficiently improves …