An ensemble of learning machines has been theoretically and empirically shown to generalise better than single learners. Diversity and accuracy are two key properties that ensemble members should possess in order for this generalisation principle to hold. Viewing these properties as objectives, we take the position of rendering multi-objective evolutionary algorithms as effective solution conce...

A considerable portion of the information on the Web is still only available in unstructured form. Implementing the vision of the Semantic Web thus requires transforming this unstructured data into structured data. One key step during this process is the recognition of named entities. Previous works suggest that ensemble learning can be used to improve the performance of named entity recognitio...

Author's response to reviews: see over

Recommender systems have achieved great success in finding relevant products and services for individual customers, e.g. in B2C markets, during recent years. However, due to the diversity of enterprise clients’ requirements it is still an open question on how to successfully apply existing recommendation techniques in the B2B domain. This paper presents GreedyBoost — an accurate, efficient and ...

accurate quantitative precipitation forecasts (qpfs) have been always a demanding and challenging job in numerical weather prediction (nwp). the outputs of ensemble prediction systems (epss) in the form of probability forecasts provide a valuable tool for probabilistic quantitative precipitation forecasts (pqpfs). in this research, different configurations of wrf and mm5 meso-scale models form ...

Removing or filtering outliers and mislabeled instances prior to training a learning algorithm has been shown to increase classification accuracy. A popular approach for handling outliers and mislabeled instances is to remove any instance that is misclassified by a learning algorithm. However, an examination of which learning algorithms to use for filtering as well as their effects on multiple ...

A new optimization technique is proposed for classifiers fusion — Cooperative Coevolutionary Ensemble Learning (CCEL). It is based on a specific multipopulational evolutionary algorithm — cooperative coevolution. It can be used as a wrapper over any kind of weak algorithms, learning procedures and fusion functions, for both classification and regression tasks. Experiments on the real-world prob...

Methods that use an !1-norm to encourage model sparsity are now widely applied across many disciplines. However, aggregating such sparse models across fits to resampled data remains an open problem. Because resampling approaches have been shown to be of great utility in reducing model variance and improving variable selection, a method able to generate a single sparse solution from multiple fit...