http://dx.doi.org/10.1016/j.ins.2014.04.013 0020-0255/ 2014 Elsevier Inc. All rights reserved. ⇑ Corresponding author. Tel.: +86 0791 88126661; fax: +86 0791 88126660. E-mail addresses: [email protected] (H. Wang), [email protected] (Z. Wu), [email protected] (S. Rahn [email protected] (H. Sun), [email protected] (Y. Liu), [email protected] (J.-s. Pan). Hui Wang a,⇑, Zhijian ...

Learning algorithms that aggregate predictions from an ensemble of diverse base classifiers consistently outperform individual methods. Many of these strategies have been developed in a supervised setting, where the accuracy of each base classifier can be empirically measured and this information is incorporated in the training process. However, the reliance on labeled data precludes the applic...

We propose a genetic ensemble of recurrent neural networks for stock prediction model. The genetic algorithm tunes neural networks in a two-dimensional and parallel framework. The ensemble makes the decision of buying or selling more conservative. It showed notable improvement on the average over not only the buy-and-hold strategy but also other traditional ensemble approaches.

A forest is an ensemble with decision trees as members. This paper proposes a novel strategy to pruning forest to enhance ensemble generalization ability and reduce ensemble size. Unlike conventional ensemble pruning approaches, the proposed method tries to evaluate the importance of branches of trees with respect to the whole ensemble using a novel proposed metric called importance gain. The i...

In the last decades ensemble learning has established itself as a valuable strategy within the computational intelligence modeling and machine learning community. Ensemble learning is a paradigm where multiple models combine in some way their decisions, or their learning algorithms, or different data to improve the prediction performance. Ensemble learning aims at improving the generalization a...

In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, th...

We address the ensemble synthesis of distributed control policies to allocate a team of homogenous robots to a collection of spatially distributed tasks. We assume individual robot controllers are derived via the sequential composition of individual task controllers and develop an appropriate macroscopic description of the team dynamics. A feedback control strategy is synthesized using the macr...

Although the Directed Hill Climbing Ensemble Pruning (DHCEP) algorithm has achieved favorable classification performance, it often yields suboptimal solutions to the ensemble pruning problem, due to its limited exploration within the whole solution space, which inspires us with the development of a novel Ensemble Pruning algorithm based on Randomized Greedy Selective Strategy and Ballot (RGSS&B...

It is shown that the ensemble {P(alpha),|alpha|alpha;{*}}, where P(alpha) is a Gaussian distribution of finite variance and |alpha is a coherent state, can be better discriminated with an entangled measurement than with any local strategy supplemented by classical communication. Although this ensemble consists of products of quasiclassical states without any squeezing, it thus exhibits a purely...