Abstract Evolutionary Ensemble Learning (EEL) provides a general approach for scaling
evolutionary learning algorithms to increasingly complex tasks. This is generally achieved …

Semantic segmentation consists of classifying each pixel of an image and constitutes an
essential step towards scene recognition and understanding. Deep convolutional encoder …

In machine learning, ensemble techniques are widely used to improve the performance of
both classification and regression systems. They combine the models generated by different …

Ensemble learning is a powerful paradigm that has been used in the top state-of-the-art
machine learning methods like Random Forests and XGBoost. Inspired by the success of …

Recent meta-learning approaches are oriented towards algorithm selection, optimization or
recommendation of existing algorithms. In this article we show how data-tailored algorithms …

When performing predictive data mining, the use of ensembles is claimed to virtually
guarantee increased accuracy compared to the use of single models. Unfortunately, the …

Artificial neural networks and symbolic expression based ensembles are used for solving
classification problems. Ensemble members and the ensembling method are generated …

Artificial neural networks based ensembles are used for solving the computer security
problems. Ensemble members and the ensembling method are generated automatically …

Intelligent information technologies help us to solve complex data mining problems and
therefore they are of particular interest. However, a generation of a specific technology …

Self-configuring genetic programming algorithm with the modified uniform crossover
operator, that realizes a selective pressure on the recombination stage, is used for the …