Ensemble Kalman filters (EnKF) based on a small ensemble tend to provide collapse of the ensemble over time. It is shown that this collapse is caused by positive coupling of the ensemble members due to use of one common estimate of the Kalman gain for the update of all ensemble members at each time step. This coupling can be avoided by resampling the Kalman gain from its sampling distribution i...

In this paper, a novel selective ensemble strategy for support vector data description (SVDD) using the Renyi entropy based diversity measure is proposed to deal with the problem of one-class classification. In order to obtain compact classification boundary, the radius of ensemble is defined as the inner product of the vector of combination weights and the vector of the radii of SVDDs. To make...

Classification is a data mining task that allocated similar data to categories or classes. One of the most general methods for classification is ensemble method which refers supervised learning. After generating classification rules we can apply those rules on unidentified data and achieve the results. In oneclass classification it is supposed that only information of one of the classes, the ta...

Despite being central to many game stories, dynamic social relationships in video games are difficult to make playable in meaningful ways. To help address this issue, this paper presents the Ensemble Engine (EE), the first publicly available “social physics” engine. The Ensemble Engine is inspired by the lessons learned from more than five years building the Comme il Faut (CiF) social physics e...

A Genetic Programming based boosting ensemble method for the classification of distributed streaming data is proposed. The approach handles flows of data coming from multiple locations by building a global model obtained by the aggregation of the local models coming from each node. A main characteristics of the algorithm presented is its adaptability in presence of concept drift. Changes in dat...

Protein remote homology detection is one of the central problems in bioinformatics. Although some computational methods have been proposed, the problem is still far from being solved. In this paper, an ensemble classifier for protein remote homology detection, called SVM-Ensemble, was proposed with a weighted voting strategy. SVM-Ensemble combined three basic classifiers based on different feat...

Concept drift has potential in smart grid analysis because the socio-economic behaviour of consumers is not governed by the laws of physics. Likewise there are also applications in wind power forecasting. In this paper we present decision tree ensemble classification method based on the Random Forest algorithm for concept drift. The weighted majority voting ensemble aggregation rule is employed...

The conventional reversible data hiding (RDH) algorithms often consider the host as a whole to embed a payload. In order to achieve satisfactory rate-distortion performance, the secret bits are embedded into the noise-like component of the host such as prediction errors. From the rate-distortion view, it may be not optimal since the data embedding units use the identical parameters. This motiva...

Deep learning has become the state of the art approach in many machine learning problems such as classi€cation. It has recently been shown that deep learning is highly vulnerable to adversarial perturbations. Taking the camera systems of self-driving cars as an example, small adversarial perturbations can cause the system to make errors in important tasks, such as classifying trac signs or det...