Ensemble classifiers originated in the machine learning community. They work by fitting many individual classifiers and combining them by weighted or unweighted voting. The ensemble classifier is often much more accurate than the individual classifiers from which it is built. In fact, ensemble classifiers are among the most accurate general-purpose classifiers available. We introduce a new ense...

Classifier fusion strategies have shown great potential to enhance the performance of pattern recognition systems. There is an agreement among researchers in classifier combination that the major factor for producing better accuracy is the diversity in the classifier team. Re-sampling based approaches like bagging, boosting and random subspace generate multiple models by training a single learn...

Background Due to rich information embedded in published articles, literature review has become an important aspect of research activities in the biomedical domain. Machine Learning (ML) techniques have been explored to retrieve relevant articles from a large literature archive (i.e., classifying articles into relevant and irrelevant classes), and to accelerating the literature review process. ...

Faculty of Engineering and Architecture Department of Electrical and Electronic Engineering Doctor of Philosophy Dataset Analysis for Classifier Ensemble Enhancement by Emanuele Tamponi We developed three different methods for dataset analysis and ensemble enhancement. They share the underlying idea that an accurate preprocessing and adaptation of the data can improve the system performance, wi...

The last ten years have seen a research explosion in machine learning. The rapid growing is largely driven by the following two forces. First, separate research communities in symbolic machine learning, computational learning theory, neural network, statistics and pattern recognition have discovered one another and begun to work together. Second, machine learning technologies are being applied ...

Diversity and accuracy are the two key factors that decide the ensemble generalization error. Constructing a good ensemble method by balancing these two factors is difficult, because increasing diversity is at the cost of reducing accuracy normally. In order to improve the performance of an ensemble while avoiding the difficulty derived of balancing diversity and accuracy, we propose a novel me...

We propose a Bayesian approach for recursively estimating the classifier weights in online learning of a classifier ensemble. In contrast with past methods, such as stochastic gradient descent or online boosting, our approach estimates the weights by recursively updating its posterior distribution. For a specified class of loss functions, we show that it is possible to formulate a suitably defi...

This paper proposes a boosting-like method to train a classifier ensemble from data streams. It naturally adapts to concept drift and allows to quantify the drift in terms of its base learners. The algorithm is empirically shown to outperform learning algorithms that ignore concept drift. It performs no worse than advanced adaptive time window and example selection strategies that store all the...

In this paper, we investigate the impact of the non-numerical information on exchange rate changes and that of ensemble multiple classifiers on forecasting exchange rate between U.S. dollar and Japanese yen. We first engage the fuzzy comprehensive evaluation model to quantify the nonnumerical fundamental information. We then design a single classifier, addressing the impact of exchange rate cha...

Machine learning classifiers are a crucial component of modern malware and intrusion detection systems. However, past studies have shown that classifier-based detection systems are susceptible to evasion attacks in practice. Improving the evasion resistance of learning based systems is an open problem. In this paper, we analyze the effects of mimicry attacks on real-world classifiers. To counte...