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Your search keyword '"Cerri, R."' showing total 17 results
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17 results on '"Cerri, R."'

4. Comparison Euromet.EM-K8 of DC Voltage Ratio: Results

Author

Marullo-Reedtz, G., primary, Cerri, R., additional, Waldmann, W., additional, Streit, J., additional, Immonen, P., additional, Blanc, I., additional, Raso, F., additional, Funck, T., additional, Schumacher, B., additional, Dierikx, E., additional, Nunes, M., additional, Vrabcek, P., additional, Rudohradsky, D., additional, Gunnarsson, O., additional, Jeanneret, B., additional, Jeckelmann, B., additional, Pulfer, T., additional, Turhan, S., additional, Yilmaz, O., additional, Williams, J., additional, Slinde, H., additional, Lind, K., additional, Nicolas, J., additional, Lindic, M., additional, Flouda, I., additional, and Erdos, G., additional

Published
2004
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17. Stacking Methods for Hierarchical Classification

Author

Sylvio Barbon, Saulo Martiello Mastelini, Felipe Kenji Nakano, Ricardo Cerri, Nakano, F. K., Martiello Mastelini, S., BARBON JUNIOR, S, and Cerri, R.

Subjects
business.industry, Computer science, 0206 medical engineering, Stacking, Decision tree, Pattern recognition, 02 engineering and technology, Task (computing), ComputingMethodologies_PATTERNRECOGNITION, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Protein function prediction, Artificial intelligence, business, 020602 bioinformatics
Abstract

Hierarchical Classification (HC) consists of classification problems whose classes are structured in a hierarchical fashion. Many problems are addressed by HC, in special a decent amount of works dealt with bioinformatics related problems such as Protein Function Prediction (PFP) and Transposable Elements (TEs) classification. Both of them are still a challenging task for HC due to the noisy and imbalanced nature of the datasets. As a countermeasure, Stacking is an ensemble method capable of generalizing knowledge from many classifiers. In this work, we propose three Stacking methods for HC and evaluate its performance on PFP and TEs datasets. Our results show that, when compared to regular Stacking and state-of-art methods from the literature, our methods are able to obtain superior or competitive performances.

Published
2017
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