Land cover monitoring using remotely sensed data requires robust classification methods
which allow for the accurate mapping of complex land cover and land use categories …

Most existing classification approaches assume the underlying training set is evenly
distributed. In class imbalanced classification, the training set for one class (majority) far …

The Maharloo watershed has witnessed many gullies in the recent due to the specific topo-
climatic conditions and man-made activities in that area. The present study is set out to …

This paper uses artificial neural networks (ANNs) based ensemble machine learning for
improving short-term electricity load forecasting. Unlike existing methods, the proposed …

Watershed management decisions need robust methods, which allow an accurate predictive
modeling of pollutant occurrences. Random Forest (RF) is a powerful machine learning data …

Most classifiers work well when the class distribution in the response variable of the dataset
is well balanced. Problems arise when the dataset is imbalanced. This paper applied four …

An ensemble consists of a set of individually trained classifiers (eg, as neural networks or
decision trees etc.) whose predictions are combined in some manner (eg, averaging or …

In this paper, a novel inverse random under sampling (IRUS) method is proposed for the
class imbalance problem. The main idea is to severely under sample the majority class thus …

The mechanism of land subsidence and soil deformation deals with the dissipation of
excess pore water pressure and the compaction of soil skeleton under the effect of natural or …

The iterative and convergent nature of ensemble learning algorithms provides potential for
improving classification of complex landscapes. This study performs land-cover …