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Context prediction, highlighted by accurate location prediction, has been at the heart of ubiquitous decision support systems. To improve the prediction accuracy of such systems, various methods have been proposed and tested; these include Bayesian networks, decision classifiers, and SVMs. Still, greater accuracy may be achieved when individual classifiers are integrated into an ensemble system...

In this paper, we propose to use classifier ensemble (CE) as a method to enhance the robustness of machine learning (ML) kernels in presence of hardware error. Different ensemble methods (Bagging and Adaboost) are explored with decision tree (C4.5) and artificial neural network (ANN) as base classifiers. Simulation results show that ANN is inherently tolerant to hardware errors with up to 10% h...

In this paper we propose a new approach for designing an ensemble applied to stream data classification. Our approach is supported by two theorems showing how to decide whether a new component should be added to the ensemble or not, based on the assumption that such an action should increase the accuracy of the ensemble not only for the current portion of observations but also for the whole (in...

In recent years, substantial improvements were obtained in the effectiveness of data driven algorithms to validate the mapping of items to skills, or the Q-matrix. In the current study we use ensemble algorithms on top of existing Qmatrix refinement algorithms to improve their performance. We combine the boosting technique with a decision tree. The results show that the improvements from both t...

Abstract A typical ensemble learning process typically uses a forward integration mechanism to construct the classifier with large number of base classifiers. Based on this mechanism, it is difficult adjust diversity among classifiers and optimize structure inside since generation has certain amount randomness, which makes performance heavily dependent human design decisions. To address issue, ...

The use of randomness in constructing decision tree ensembles has drawn much attention in the machine learning community. In general, ensembles introduce randomness to generate diverse trees and in turn they enhance ensembles’ predictive accuracy. Examples of such ensembles are Bagging, Random Forests and Random Decision Tree. In the past, most of the random tree ensembles inject various kinds ...