Ensemble techniques have proved to be very useful to boost the performance of several types of machine learning methods. In this paper, we illustrate its usefulness in combination with GAssist, a Pittsburgh-style Learning Classifier System. Two types of ensemble are tested. First baggingstyle consensus prediction. Second an ensemble intended to deal more efficiently with ordinal classification ...

High dimensionality is a major problem that affects the quality of training datasets and therefore classification models. Feature selection is frequently used to deal with this problem. The goal of feature selection is to choose the most relevant and important attributes from the raw dataset. Another major challenge to building effective classification models from binary datasets is class imbal...

Educational institutions have dramatically increased in recent years, producing many graduates and postgraduates each year. One of the critical concerns decision-makers is student performance. data mining techniques are beneficial to explore uncovered itself, creating a pattern analyze In this study, we investigate E-learning that has significantly era COVID-19. Thus, study aims predict perform...

Recently many statistical learning techniques are successfully developed and used in several areas. However, these algorithms sometimes are not robust and does not show good performances. The ensemble method can solve these problems. It is known that the ensemble learning sometimes improves the generalized performance of machine learning tasks as well as makes it robust. However, the combining ...

In the last decades ensemble learning has established itself as a valuable strategy within the computational intelligence modeling and machine learning community. Ensemble learning is a paradigm where multiple models combine in some way their decisions, or their learning algorithms, or different data to improve the prediction performance. Ensemble learning aims at improving the generalization a...

Background and Purpose: Nowadays, breast cancer is reported as one of the most common cancers amongst women. Early detection of the cancer type is essential to aid in informing subsequent treatments. The newest proposed breast cancer detectors are based on deep learning. Most of these works focus on large-datasets and are not developed for small datasets. Although the large datasets might lead ...

Ensemble learning aims to improve the generalization power and the reliability of learner models through sampling and optimization techniques. It has been shown that an ensemble constructed by a selective collection of base learners outperforms favorably. However, effective implementation of such an ensemble from a given learner pool is still an open problem. This paper presents an evolutionary...

The problem of concept drift has recently received considerable attention in machine learning research. One important practical problem where concept drift needs to be addressed is spam filtering. The literature on concept drift shows that among the most promising approaches are ensembles and a variety of techniques for ensemble construction has been proposed. In this paper we consider an alter...

Ensemble classification methods that independently construct component models (e.g., bagging) improve accuracy over single models by reducing the error due to variance. Some work has been done to extend ensemble techniques for classification in relational domains by taking relational data characteristics or multiple link types into account during model construction. However, since these approac...