Quantum machine learning has shown advantages in many ways compared to classical learning. In learning, a difficult problem is how learn model with high robustness and strong generalization ability from limited feature space. Combining multiple models as base learners, ensemble (EL) can effectively improve the accuracy, of final model. The key EL lies two aspects, performance learners choice co...

In this paper we designed and implemented a new ensemble of classifiers based on a sequence of classifiers which were specialized in regions of the training dataset where errors of its trained homologous are concentrated. In order to separate this regions, and to determine the aptitude of each classifier to properly respond to a new case, it was used another set of classifiers built hierarchica...

In this paper we propose a new document classification method, bridging discrepancies (so-called semantic gap) between the training set and the application sets of textual data. We demonstrate its superiority over classical text classification approaches, including traditional classifier ensembles. The method consists in combining a document categorization technique with a single classifier or ...

The relationship between ensemble classifier performance and the diversity of the predictions made by ensemble base classifiers is explored in the context of heterogeneous ensemble classifiers. Specifically, numerical studies indicate that heterogeneous ensembles can be generated from base classifiers of homogeneous ensemble classifiers that are both significantly more accurate and diverse than...

An ensemble is a collective decision-making system which applies a strategy to combine the predictions of learned classifiers to generate its prediction of new instances. Early research has proved that ensemble classifiers in most cases can be more accurate than any single component classifier both empirically and theoretically. Though many ensemble approaches are proposed, it is still not an e...

Defective modules in the software pose considerable risk by decreasing customer satisfaction and by increasing the development and maintenance costs. Therefore, in software development life cycle, it is essential to predict defective modules in the early stage so as to improve software developers' ability to identify the defect-prone modules and focus quality assurance activities. Many res...

A Classifier Ensemble (CE) efficiently improves the generalization ability of the classifier compared to a single classifier. This paper proposes an alternate approach for Integration of classifier ensembles. Initially three classifiers that are highly diverse and showed good classification accuracy when applied to six UCI (University of California, Irvine) datasets are selected. Then Feature S...

Mammography is currently the most effective imaging modality for early detection of breast cancer. In a CAD system for masses based on mammography, a mammogram is segmented to detect the masses. The segmentation gives rise to mass regions of interested (ROIs), which are either benign or malignant. There is a need to classify the extracted mass ROIs into benign and malignant masses; it is a hard...