The problem of training classifiers only with target data arises in many applications where non-target data are too costly, difficult to obtain, or not available at all. Several one-class classification methods have been presented to solve this problem, but most of the methods are highly sensitive to the presence of outliers in the target class. Ensemble methods have therefore been proposed as ...

[1] The interactive ensemble coupling strategy has been developed specifically to determine how noise impacts climate variability within context of coupled general circulation model (CGCM). This study examines the impacts of local versus non-local noise on the North Pacific sea surface temperature anomaly (SSTA) variability using three CGCM simulations. The control run uses the standard couplin...

Recently, besides the performance, the stability (robustness, i.e., the variation in feature selection results due to small changes in the data set) of feature selection is received more attention. Ensemble feature selection where multiple feature selection outputs are combined to yield more robust results without sacrificing the performance is an effective method for stable feature selection. ...

An extension of a regression-based methodology for constraining climate forecasts using a multi-thousand member ensemble of perturbed climate models is presented, using the multi-model CMIP-3 ensemble to estimate the systematic model uncertainty in the prediction, with the caveat that systematic biases common to all models are not accounted for. It is shown that previous methodologies for estim...

Robustness of feature selection techniques is a topic of recent interest, especially in high dimensional domains with small sample sizes, where selected feature subsets are subsequently analysed by domain experts to gain more insight into the problem modelled. In this work, we investigate the robustness of various feature selection techniques, and provide a general scheme to improve robustness ...

Ensemble clustering is a recently evolving research direction in cluster analysis and has found several different application domains. In this work the complex ensemble clustering problem is reduced to the well-known Euclidean median problem by clustering embedding in vector spaces. The Euclidean median problem is solved by the Weiszfeld algorithm and an inverse transformation maps the Euclidea...

In this paper, we propose an ensemble classification approach to the Pedestrian Detection (PD) problem, resorting to distinct input channels and Convolutional Neural Networks (CNN). This methodology comprises two stages: piq the proposals extraction, and piiq the ensemble classification. In order to obtain the proposals, we apply several detectors specifically developed for the PD task. Afterwa...

We present a novel online ensemble learning strategy for portfolio selection. The new strategy controls and exploits any set of commission-oblivious portfolio selection algorithms. The strategy handles transaction costs using a novel commission avoidance mechanism. We prove a logarithmic regret bound for our strategy with respect to optimal mixtures of the base algorithms. Numerical examples va...