Deep learning methods have become the de-facto standard for challenging image
processing tasks such as image classification. One major hurdle of deep learning …

Fault diagnosis of rotating machinery components under different working conditions or
noisy environment has been a major challenge. The domain shift caused by fluctuation of …

Data mining and machine learning algorithms can be employed to perform a variety of tasks.
However, since most of these problems may depend on environments that change over …

Background Identifying splice site regions is an important step in the genomic DNA
sequencing pipelines of biomedical and pharmaceutical research. Within this research …

Bioinformatics analysis based on microarray technology is facing serious challenges, due to
the extremely high dimensionality of the gene expression data comparing to the typical small …

Supervised neural approaches are hindered by their dependence on large, meticulously
annotated datasets, a requirement that is particularly cumbersome for sequential tasks. The …

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 …

This thesis is focused on the analysis and development of Ensembles of Neural Networks.
An ensemble is a system in which a set of heterogeneous Artificial Neural Networks are …

The test set accuracy for ensembles of classifiers selected based on single measures of
accuracy and diversity as well as combinations of such measures is investigated. It is found …

Both theory and a wealth of empirical studies have established that ensembles are more
accurate than single predictive models. Unfortunately, the problem of how to maximize …