Ensemble learning is a prolific field in Machine Learning since it is based on the assumption
that combining the output of multiple models is better than using a single model, and it …

Feature selection is an important topic in machine learning. In recent years, via integrating
ensemble learning, the ensemble learning based feature selection approach has been …

In the last decade, ensemble learning has become a prolific discipline in pattern recognition,
based on the assumption that the combination of the output of several models obtains better …

Feature selection has become the essential step in many data mining applications. Using a
single feature subset selection method may generate local optima. Ensembles of feature …

Crop cultivation prediction is an integral part of agriculture and is primarily based on factors
such as soil, environmental features like rainfall and temperature, and the quantum of …

In recent years, ensemble learning has become a prolific area of study in pattern
recognition, based on the assumption that using and combining different learning models in …

Software defect prediction models are used to identify program modules that are high-risk, or
likely to have a high number of faults. These models are built using software metrics which …

In the last few years, ensemble learning has been the focus of much attention mainly in
classification tasks, based on the assumption that combining the output of multiple experts is …

Cell phones are an emerging but rapidly growing area of computer forensics. While cell
phones are becoming more like desktop computers functionally, their organization and …

Two of the most challenging problems in data mining are working with imbalanced datasets
and with datasets which have a large number of attributes. In this study we compare three …