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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 Software Systems

+41 (0) 58 934 76 13

Personal profile

Position at the ZHAW

Research Associate

Expertise and research interests

Machine learning, data mining, text mining

Educational background

PhD, University of Konstanz, 2017
Certificate of Advanced Studies in Didaktik-Methodik 2018
Dipl. Inform., University of Bielefeld, 2007

Professional milestones

Organizing member of SwissText 2018
Organizing member of SwissText 2017


Publications and conference contributions

Other publications

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

Publications before appointment at the ZHAW

Improving scalability of ART neural networks, F Benites, E Sapozhnikova, Neurocomputing 230, 219-229, 2017, DOI:

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.