Understanding ensemble diversity is one of the most important fundamental issues in ensemble learning. Inspired by a recent work trying to explain ensemble diversity from the information theoretic perspective, in this paper we study the ensemble diversity from the view of multi-information. We show that from this view, the ensemble diversity can be decomposed over the component classifiers cons...

Defensive forecasting is a method of transforming laws of probability (stated in game-theoretic terms as strategies for Sceptic) into forecasting algorithms. There are two known varieties of defensive forecasting: “continuous”, in which Sceptic’s moves are assumed to depend on the forecasts in a (semi)continuous manner and which produces deterministic forecasts, and “randomized”, in which the d...

Facial action unit (au) classification is an approach to face expression recognition that decouples the recognition of expression from individual actions. In this paper, upper face aus are classified using an ensemble of MLP (Multi-layer perceptron) base classifiers with feature ranking based on PCA components. This approach is compared experimentally with other popular feature-ranking methods ...

This paper considers the correction of deterministic forecasts given by a flood forecasting model. A stochastic correction based on the evolution of an adaptive, multiplicative, gain is presented. A number of models for the evolution of the gain are considered and the quality of the resulting probabilistic forecasts assessed. The techniques presented offer a computationally efficient method for...

he growing popularity of online product review forums invites the development of models and metrics that allow firms to harness these new sources of information for decision support. Our work contributes in this direction by proposing a novel family of diffusion models that capture some of the unique aspects of the entertainment industry and testing their performance in the context of very earl...

We derive generalization error bounds — bounds on the expected inaccuracy of the predictions — for traditional time series forecasting models. Our results hold for many standard forecasting tools including autoregressive models, moving average models, and, more generally, linear state-space models. These bounds allow forecasters to select among competing models and to guarantee that with high p...

An accurate and stable short-term traffic forecasting model is very important for intelligent transportation systems (ITS). The forecasting results can be used to relieve traffic congestion and improve the mobility of transportation. This paper proposes a new hybrid model of grey system theory and neural networks with particle swarm optimization, namely, GNN-PSO. The proposed hybrid model can e...

1 Singh, S. "Forecasting using a Fuzzy Nearest Neighbour Method", Proc. 6th International Conference on Fuzzy Theory and Technology , Fourth Joint Conference on Information Sciences (JCIS'98), North Carolina, vol. 1, pp.80-83, 1998 (23-28 October ,1998) ABSTRACT This paper explores a nearest neighbour pattern recognition method for time-series forecasting. A nearest neighbour method (FNNM) base...