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About

Pavel Brazdil is a founder of a strong Machine Learning / Data Mining group that exists since 1988 and which now is a part of LIAAD Inesc Tec (Laboratory of AI and Decision Support). Pavel Brazdil is Full Professor (Prof. Catedrático) at the Faculty of Economics (FEP) of University of Porto, where he has been teaching courses on Information systems, Data Mining and Text Mining. He has supervised 12 PhD students. Although he has officially retired in mid-July 2015, he continues his R&D activities, including teaching at Master and Doctoral courses and supervision of post-graduate students.

Interest
Topics
Details

Details

  • Name

    Pavel Brazdil
  • Cluster

    Computer Science
  • Role

    Affiliated Researcher
  • Since

    01st January 2010
001
Publications

2018

Speeding up algorithm selection using average ranking and active testing by introducing runtime

Authors
Abdulrahman, SM; Brazdil, P; van Rijn, JN; Vanschoren, J;

Publication
Machine Learning

Abstract

2018

Metalearning and Algorithm Selection: progress, state of the art and introduction to the 2018 Special Issue

Authors
Brazdil, P; Giraud Carrier, C;

Publication
Machine Learning

Abstract
This article serves as an introduction to the Special Issue on Metalearning and Algorithm Selection. The introduction is divided into two parts. In the the first section, we give an overview of how the field of metalearning has evolved in the last 1–2 decades and mention how some of the papers in this special issue fit in. In the second section, we discuss the contents of this special issue. We divide the papers into thematic subgroups, provide information about each subgroup, as well as about the individual papers. Our main aim is to highlight how the papers selected for this special issue contribute to the field of metalearning. © 2017 The Author(s)

2018

Incremental TextRank - Automatic Keyword Extraction for Text Streams

Authors
Sarmento, RP; Cordeiro, M; Brazdil, P; Gama, J;

Publication
Proceedings of the 20th International Conference on Enterprise Information Systems, ICEIS 2018, Funchal, Madeira, Portugal, March 21-24, 2018, Volume 1.

Abstract

2018

Impact of Feature Selection on Average Ranking Method via Metalearning

Authors
Abdulrahman, SM; Cachada, MV; Brazdil, P;

Publication
VIPIMAGE 2017

Abstract
Selecting appropriate classification algorithms for a given dataset is crucial and useful in practice but is also full of challenges. In order to maximize performance, users of machine learning algorithms need methods that can help them identify the most relevant features in datasets, select algorithms and determine their appropriate hyperparameter settings. In this paper, a method of recommending classification algorithms is proposed. It is oriented towards the average ranking method, combining algorithm rankings observed on prior datasets to identify the best algorithms for a new dataset. Our method uses a special case of data mining workflow that combines algorithm selection preceded by a feature selection method (CFS).

2018

Incremental TextRank - Automatic Keyword Extraction for Text Streams

Authors
Sarmento, RP; Cordeiro, M; Brazdil, P; Gama, J;

Publication
Proceedings of the 20th International Conference on Enterprise Information Systems, ICEIS 2018, Funchal, Madeira, Portugal, March 21-24, 2018, Volume 1.

Abstract
Text Mining and NLP techniques are a hot topic nowadays. Researchers thrive to develop new and faster algorithms to cope with larger amounts of data. Particularly, text data analysis has been increasing in interest due to the growth of social networks media. Given this, the development of new algorithms and/or the upgrade of existing ones is now a crucial task to deal with text mining problems under this new scenario. In this paper, we present an update to TextRank, a well-known implementation used to do automatic keyword extraction from text, adapted to deal with streams of text. In addition, we present results for this implementation and compare them with the batch version. Major improvements are lowest computation times for the processing of the same text data, in a streaming environment, both in sliding window and incremental setups. The speedups obtained in the experimental results are significant. Therefore the approach was considered valid and useful to the research community. Copyright

Supervised
thesis

2017

Workflow Recommendation for Text Classification Problems

Author
Maria João Fernandes Ferreira

Institution
UP-FCUP

2017

Automatic Recommendation of Machine Learning Workflows

Author
Miguel Alexandre Viana Cachada

Institution
UP-FEP

2017

Improving Algorithm Selection Methods using Meta-Learning by Considering Accuracy and Run Time

Author
Salisu Mamman Abdulrahman

Institution
UP-FEP

2017

Identifying Affinity Groups of Researchers in FEP through the Application of Community Detection Algorithms

Author
André Martinez Candeias Lima

Institution
UP-FEP

2015

Análise de comentários de clientes com o auxílio a técnicas de Text Mining para determinar o nível de (in)satisfação

Author
Ana Catarina Barbosa Forte

Institution
UP-FEP