Emanuele Tamponi

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The performance of a classification system depends on various aspects, including encoding techniques. In fact, encoding techniques play a primary role in the process of tuning a classifier/predictor, as choosing the most appropriate encoder may greatly affect its performance. As of now, evaluating the impact of an encoding technique on a classification(More)
Taxonomies are becoming essential in several fields, playing an important role in a large number of applications, particularly for specific domains. Taxonomies provide efficient tools to people by organizing a huge amount of information into a small hierarchical structure. Taxonomies were originally built by hand, but nowadays the technology permits to(More)
Faculty of Engineering and Architecture Department of Electrical and Electronic Engineering Doctor of Philosophy Dataset Analysis for Classifier Ensemble Enhancement by Emanuele Tamponi We developed three different methods for dataset analysis and ensemble enhancement. They share the underlying idea that an accurate preprocessing and adaptation of the data(More)
Systems for assessing the classification complexity of a dataset have received increasing attention in research activities on pattern recognition. These systems typically aim at quantifying the overall complexity of a domain, with the goal of comparing different datasets. In this work, we propose a method for partitioning a dataset into regions of different(More)
Motivations Defining an optimal encoding for input data is fundamental to achieve high performances in prediction tasks. Its main responsibility is to transform input data to a format suitable for the classification algorithm. The selection of the best encoding is typically done by resorting to the knowledge of a human expert, entrusted with extracting the(More)
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