there are three major strategies to form neural network ensembles. the simplest one is the cross validation strategy in which all members are trained with the same training data. bagging and boosting strategies pro-duce perturbed sample from training data. this paper provides an ideal model based on two important factors: activation function and number of neurons in the hidden layer and based u...

We propose a machine learning-based methodology which makes use of ensemble methods with the aims (i) treating missing data in time series irregular observation times and detecting anomalies observed behavior; (ii) defining suitable models system dynamics. applied this to US wholesale electricity price that are characterized by data, high stochastic volatility, jumps pronounced spikes. For we p...

Poverty is a social-cultural problem that can be categorized into monetary approach, capability social exclusion, and participatory poverty assessment. However, the existing measurement methods are complex, costly, time-consuming. This research was conducted to forecast using classification methods. Random Forest Extreme Gradient Boosting (XGBoost) algorithms were applied since they supervised ...

In this work we present a novel approach to ensemble learning for regression models, by combining the ensemble generation technique of random subspace method with the ensemble integration methods of Stacked Regression and Dynamic Selection. We show that for simple regression methods such as global linear regression and nearest neighbours, this is a more effective method than the popular ensembl...

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are ana...

Ensemble selection deals with the reduction of an ensemble of predictive models in order to improve its efficiency and predictive performance. A number of ensemble selection methods that are based on greedy search of the space of all possible ensemble subsets have recently been proposed. This paper contributes a novel method, based on a new diversity measure that takes into account the strength...