High dimensionality is a major problem that affects the quality of training datasets and therefore classification models. Feature selection is frequently used to deal with this problem. The goal of feature selection is to choose the most relevant and important attributes from the raw dataset. Another major challenge to building effective classification models from binary datasets is class imbal...

Ensemble learning aims at combining several slightly different learners to construct stronger learner. Ensemble of a well selected subset of learners would outperform than ensemble of all. However, the well studied accuracy / diversity ensemble pruning framework would lead to over fit of training data, which results a target learner of relatively low generalization ability. We propose to ensemb...

Deep learning systems have dramatically improved the accuracy of speech recognition, and various deep architectures and learning methods have been developed with distinct strengths and weaknesses in recent years. How can ensemble learning be applied to these varying deep learning systems to achieve greater recognition accuracy is the focus of this paper. We develop and report linear and log-lin...

This paper investigates an ensemble feature selection algorithm that is based on genetic algorithms. The task of ensemble feature selection is harder than traditional feature selection in that one not only needs to find features germane to the learning task and learning algorithm, but one also needs to find a set of feature subsets that will promote disagreement among the ensemble’s classifiers...

Ensemble techniques have proved to be very useful to boost the performance of several types of machine learning methods. In this paper, we illustrate its usefulness in combination with GAssist, a Pittsburgh-style Learning Classifier System. Two types of ensemble are tested. First baggingstyle consensus prediction. Second an ensemble intended to deal more efficiently with ordinal classification ...

In this study, we applied ensemble machine learning to predict pitch accents. With decision tree as the baseline algorithm, two popular ensemble learning methods, bagging and boosting, were evaluated across different experiment conditions: using acoustic features only, using text-based features only; using both acoustic and text-based features. F0 related acoustic features are derived from unde...

This paper introduces Deep Incremental Boosting, a new technique derived from AdaBoost, specifically adapted to work with Deep Learning methods, that reduces the required training time and improves generalisation. We draw inspiration from Transfer of Learning approaches to reduce the start-up time to training each incremental Ensemble member. We show a set of experiments that outlines some prel...

An ensemble learning algorithm was proposed in this paper by analyzing the error function of neural network ensembles, by which, individual neural networks were actively guided to learn diversity. By decomposing the ensemble error function, error correlation terms were included in the learning criterion function of individual networks. And all the individual networks in the ensemble were leaded...