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Dr. Fernando Benites de Azevedo e Souza

Dr. Fernando Benites de Azevedo e Souza

Dr. Fernando Benites de Azevedo e Souza
ZHAW School of Engineering
Forschungsschwerpunkt Distributed Software Systems

+41 (0) 58 934 76 13
fernando.benites@zhaw.ch

Persönliches Profil

Tätigkeit an der ZHAW als

Wissenschaftlicher Mitarbeiter

http://www.init.zhaw.ch/

Arbeits- und Forschungsschwerpunkte, Spezialkenntnisse

Maschinelles Lernen, Data Mining, Text Mining

Aus- und Fortbildung

Dr. rer. nat., Universität Konstanz, 2017
Certificate of Advanced Studies in Didaktik-Methodik 2018
Dipl. Inform., Universität Bielefeld, 2007

Beruflicher Werdegang

Organizing member of SwissText 2018
Organizing member of SwissText 2017

Projekte

Publikationen und Konferenzbeiträge

Übrige Publikationen

BENITES DE AZEVEDO E SOUZA, Fernando, 2017. Multi-label Classification with Multiple Class Ontologies [Dissertation]. Konstanz: University of Konstanz

Publikationen vor Tätigkeit an der ZHAW

Improving scalability of ART neural networks, F Benites, E Sapozhnikova, Neurocomputing 230, 219-229, 2017, DOI:https://doi.org/10.1016/j.neucom.2016.12.022

HARAM: a Hierarchical ARAM neural network for large-scale text classification, F Benites, E Sapozhnikova, IEEE International Conference on Data Mining Workshop (ICDMW), The 3rd International Workshop on High Dimensional Data Mining (HDM’15), 847-854, 2015.

Hierarchical interestingness measures for association rules with generalization on both antecedent and consequent sides, F Benites, E Sapozhnikova, Pattern Recognition Letters 65, 197-203, 2015.

Improving Multi-label Classification by Means of Cross-Ontology Association Rules,F Benites, E Sapozhnikova, Workshop on Knowledge Discovery, Data Mining and Machine Learning, LWA/KDML 15,Volume: 1458, 2015.

Evaluation of hierarchical interestingness measures for mining pairwise generalized association rules, F Benites, E Sapozhnikova, IEEE Transactions on Knowledge and Data Engineering 26 (12), 3012-3025, 5, 2014.

Using Semantic Data Mining for Classification Improvement and Knowledge Extraction., F Benites, EP Sapozhnikova, LWA, 150-155, 1, 2014.

Mining rare associations between biological ontologies, F Benites, S Simon, E Sapozhnikova, PloS one 9 (1), e84475, 12, 2014.

Generalized Association Rules for Connecting Biological Ontologies., F Benites, EP Sapozhnikova, BIOINFORMATICS, 229-236, 2, 2013.

Learning different concept hierarchies and the relations between them from classified data, F Benites, E Sapozhnikova, Intel. Data Analysis for Real-Life Applications: Theory and Practice, 18-34, 5, 2012.

Multi-label classification and extracting predicted class hierarchies, F Brucker, F Benites, E Sapozhnikova, Pattern Recognition 44 (3), 724-738, 29, 2011.

An empirical comparison of flat and hierarchical performance measures for multi-label classification with hierarchy extraction, F Brucker, F Benites, E Sapozhnikova, Knowledge-Based and Intelligent Information and Engineering Systems, 579-589, 7, 2011.

Multi-label classification by ART-based neural networks and hierarchy extraction, F Benites, F Brucker, E Sapozhnikova, Neural Networks (IJCNN), The 2010 International Joint Conference on, 1-9.