One of the most relevant task in Sentiment Analysis is Polarity Classification. In this paper, we discuss how to explore the potential of ensembles of classifiers and propose a voting mechanism based on Bayesian Model Averaging (BMA). An important issue to be addressed when using ensemble classification is the model selection strategy. In order to help in selecting the best ensemble composition...

We derive a new achievable rate region for the problem of communicating over a multiple access channel with states. Our coding technique is based on the ensemble of nested coset codes and the technique of typicality decoding. Exploiting structure in this ensemble, we analyze a more efficient decoding strategy to improve upon the rate region achievable using unstructured codes. We identify examp...

This paper presents a novel method for facial expression recognition that employs the combination of two different feature sets in an ensemble approach. A pool of base support vector machine classifiers is created using Gabor filters and Local Binary Patterns. Then a multi-objective genetic algorithm is used to search for the best ensemble using as objective functions the minimization of both t...

Recent ensemble clustering techniques have been shown to be effective in improving the accuracy and stability of standard clustering algorithms. However, an inherent drawback of these techniques is the computational cost of generating and combining multiple clusterings of the data. In this paper, we present an efficient kernel-based ensemble clustering method suitable for application to large, ...

Stock prices as time series are, often, non-linear and non-stationary. This paper presents an ensemble forecasting model that integrates Empirical Mode Decomposition (EMD) and its variation Ensemble Empirical Mode Decomposition (EEMD) with Artificial Neural Network (ANN) for short-term forecasts of stock index. In first stage, the data is decomposed into a smaller set of Intrinsic Mode Function...

Ranking potential customers has become an effective tool for company decision makers to design marketing strategies. The task of PAKDD competition 2007 is a cross-selling problem between credit card and home loan, which can also be treated as a ranking potential customers problem. This article proposes a 3-level ranking model, namely Group-Ensemble, to handle such kinds of problems. In our mode...

Ensemble methods (or simply ensembles) for motif discovery represent a relatively new approach to improve the accuracy of standalone motif finders. In particular, the accuracy of an ensemble is determined by the included finders and the strategy (learning rule) used to combine the results returned by the latter, making these choices crucial for the ensemble success. In this research we propose ...

Feature selection for ensembles has shown to be an effective strategy for ensemble creation due to its ability of producing good subsets of features, which make the classifiers of the ensemble disagree on difficult cases. In this paper we present an ensemble feature selection approach based on a hierarchical multi-objective genetic algorithm. The algorithm operates in two levels. Firstly, it pe...

The literature suggests that an ensemble of classifiers outperforms a single classifier across a range of classification problems. This paper investigates the application of an ensemble of neural network classifiers to the prediction of potential defaults for a set of personal loan accounts drawn from a medium sized Australian financial institution. The imbalanced nature of the data sets necess...