The diversity of an ensemble can be calculated in a variety of ways. Here a diversity metric and a means for altering the diversity of an ensemble, called “thinning”, are introduced. We experiment with thinning algorithms evaluated on ensembles created by several techniques on 22 publicly available datasets. When compared to other methods, our percentage correct diversity measure algorithm show...

The article suggests an algorithm for regular classifier ensemble methodology. The proposed methodology is based on possibilistic aggregation to classify samples. The argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. The optimization aims at learning backgrounds as solid clusters in subspaces of the high...

We describe the use of ensemble methods to build proper models time series prediction. Our approach extends the classical ensemble methods for neural networks by using several different model architectures. We further suggest an iterated prediction procedure to select the final ensemble members. This is an extension of well know the crossvalidation scheme for model validation.

architecture has been always recognized as a framework which is influenced by social beliefs, traditions and interactions and furthermore affects individual soul and tendency. therefore it is impossible to analyze and consequently identify an architectural artifact if not accompanied by analysis of intellectual fundamentals of its contemporary society. the ensemble of sheikh safi al-din which i...

Two classes of state estimation schemes, variational (4DVar) and ensemble Kalman (EnKF), have been developed and used extensively by the weather forecasting community as tractable alternatives to the standard matrix-based Kalman update equations for the estimation of high-dimensional nonlinear systems with possibly nongaussian PDFs. Variational schemes iteratively minimize a finite-horizon cost...

Ensemble and summary displays are two widely used methods to represent visual-spatial uncertainty; however, there is disagreement about which is the most effective technique to communicate uncertainty to the general public. Visualization scientists create ensemble displays by plotting multiple data points on the same Cartesian coordinate plane. Despite their use in scientific practice, it is mo...

Ensemble simulations and forecasts provide probabilistic information about the inherently uncertain climate system. Counting the number of ensemble members in a category is a simple nonparametric method of using an ensemble to assign categorical probabilities. Parametric methods of assigning quantile-based categorical probabilities include distribution fitting and generalized linear regression....

Option pricing based on data-driven methods is a challenging task that has attracted much attention recently. There are mainly two types of have been widely used, respectively, the neural network method and ensemble learning method. The option model high complexity, large number hyper-parameters will be generated during training, resulting in difficult adjustment. Furthermore, lot training data...