Background and Objectives: According to the random nature of heuristic algorithms, stability analysis of heuristic ensemble classifiers has particular importance. Methods: The novelty of this paper is using a statistical method consists of Plackett-Burman design, and Taguchi for the first time to specify not only important parameters, but also optimal levels for them. Minitab and Design Expert ...

In this study, a novel weighted ensemble classifier that improves classification accuracy and minimizes the number of classifiers is proposed. Proposed method uses sparsity techniques therefore it is named sparsity-driven weighted ensemble classifier (SDWEC). In SDWEC, ensemble weight finding problem is modeled as a cost function with following terms: (a) a data fidelity term aiming to decrease...

There are so many social networking sites available. Tweets have evolved into a crucial tool for gathering people's thoughts, ideas, behaviours and sentiments surrounding particular entities. One of the most intriguing subjects in this context is analyzing sentiment tweets using natural language processing (NLP). Although several methods been created, accuracy effectiveness those analysis yet t...

Multi-layer perceptrons (MLP) make powerful classifiers that may provide superior performance compared with other classifiers, but are often criticized for the number of free parameters. Most commonly, parameters are set with the help of either a validation set or cross-validation techniques, but there is no guarantee that a pseudo-test set is representative. Further difficulties with MLPs incl...

To maximize the benefit that can be derived from the information implicit in big data, ensemble methods generate multiple models with sufficient diversity through randomization or perturbation. A k-dependence Bayesian classifier (KDB) is a highly scalable learning algorithm with excellent time and space complexity, along with high expressivity. This paper introduces a new ensemble approach of K...

This paper is an attempt to increase the understanding in the behavior of ensembles for discrete variables in a quantitative way. A set of tight upper and lower bounds for the accuracy of an ensemble is presented for wide classes of ensemble algorithms, including bagging and boosting. The ensemble accuracy is expressed in terms of the accuracies of the members of the ensemble. Since those bound...