Material decomposition facilitates the differentiation of different materials in X-ray imaging. As an alternative to the previous empirical material decomposition methods, we performed material decomposition using ensemble learning methods in this work. Three representative ensemble methods with two decision trees as the base learning algorithms were implemented to perform material decompositio...

In this paper, we present two ensemble learning algorithms which make use of boostrapping and out-of-bag estimation in an attempt to inherit the robustness of bagging to overfitting. As against bagging, with these algorithms learners have visibility on the other learners and cooperate to get diversity, a characteristic that has proved to be an issue of major concern to ensemble models. Experime...

This thesis studies the diversity issue of classification ensembles for class imbalance learning problems. Class imbalance learning refers to learning from imbalanced data sets, in which some classes of examples (minority) are highly under-represented comparing to other classes (majority). The very skewed class distribution degrades the learning ability of many traditional machine learning meth...

The Ensemble classifier has been an active research topic in the area of machine learning. In a classification task, the ensemble scheme determines a final class label from several individual results which are usually generated by several individual classifiers according to two major principles. The first is to use multiple learning algorithms to form the set of individual outcomes, and the sec...

In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We present an adaptation of several popular ensemble techniques in machine learnin...

The brain exploits redundancies in the environment to efficiently represent the complexity of the visual world. One example of this is ensemble processing, which provides a statistical summary of elements within a set (e.g., mean size). Another is statistical learning, which involves the encoding of stable spatial or temporal relationships between objects. It has been suggested that ensemble pr...

Representation learning and unsupervised learning are two central topics of machine learning and signal processing. Deep learning is one of the most effective unsupervised representation learning approach. The main contributions of this paper to the topics are as follows. (i) We propose to view the representative deep learning approaches as special cases of the knowledge reuse framework of clus...

The success of simple methods for classification shows that is is often not necessary to model complex attribute interactions to obtain good classification accuracy on practical problems. In this paper, we propose to exploit this phenomenon in the data stream context by building an ensemble of Hoeffding trees that are each limited to a small subset of attributes. In this way, each tree is restr...

The problem of generalizing deep neural networks from multiple source domains to a target one is studied under two settings: When unlabeled data available, it multi-source unsupervised domain adaptation (UDA) problem, otherwise generalization (DG) problem. We propose unified framework termed adaptive ensemble learning (DAEL) address both problems. A DAEL model composed CNN feature extractor sha...