BACKGROUND microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each ...

Abstract Clustering is fundamental to understand the structure of data. In the past decade the cluster ensemble problem has been introduced, which combines a set of partitions (an ensemble) of the data to obtain a single consensus solution that outperforms all the ensemble members. However, there is disagreement about which are the best ensemble characteristics to obtain a good performance: som...

Depending on how a learner reacts to the test instances, supervised learning divided into eager learning and lazy learning. Lazy learners endeavor to find local optimal solutions for each particular test instance. Many approaches for constructing lazy learning have been developed, one of the successful one is to incorporate lazy learning with ensemble classification. Almost all lazy learning sc...

Ensemble methods, which combine several classifiers, have been successfully applied to decrease generalization error of machine learning methods. For most ensemble methods the ensemble members are combined by weighted summation of the output, called the linear average predictor. The logarithmic opinion pool ensemble method uses a multiplicative combination of the ensemble members, which treats ...

The growing use of multi-channel neural recording techniques in behaving animals has produced rich datasets that hold immense potential for advancing our understanding of how the brain mediates behavior. One limitation of these techniques is they do not provide important information about the underlying anatomical connections among the recorded neurons within an ensemble. Inferring these connec...

Subsemble: A Flexible Subset Ensemble Prediction Method

The aim of this paper is to propose an application of mutual information-based ensemble methods to the analysis and classification of heart beats associated with different types of Arrhythmia. Models of multilayer perceptrons, support vector machines, and radial basis function neural networks were trained and tested using the MIT-BIH arrhythmia database. This research brings a focus to an ensem...

1. Introduction Since its first introduction by Evensen (1994), the ensemble Kalman filter (EnKF) technique for data assimilation has received much attention. Rather than solving the equation for the time evolution of the probability density function of model state, the EnKF methods apply the Monte Carlo method to estimate the forecast error statistics. A large ensemble of model states are inte...

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...