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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

2020

Incremental Approach for Automatic Generation of Domain-Specific Sentiment Lexicon

Authors
Muhammad, SH; Brazdil, P; Jorge, A;

Publication
Lecture Notes in Computer Science - Advances in Information Retrieval

Abstract

2019

Identifying, Ranking and Tracking Community Leaders in Evolving Social Networks

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

Publication
Complex Networks and Their Applications VIII - Volume 1 Proceedings of the Eighth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019, Lisbon, Portugal, December 10-12, 2019.

Abstract
Discovering communities in a network is a fundamental and important problem to complex networks. Find the most influential actors among its peers is a major task. If on one side, studies on community detection ignore the influence of actors and communities, on the other hand, ignoring the hierarchy and community structure of the network neglect the actor or community influence. We bridge this gap by combining a dynamic community detection method with a dynamic centrality measure. The proposed enhanced dynamic hierarchical community detection method computes centrality for nodes and aggregated communities and selects each community representative leader using the ranked centrality of every node belonging to the community. This method is then able to unveil, track, and measure the importance of main actors, network intra and inter-community structural hierarchies based on a centrality measure. The empirical analysis performed, using two temporal networks shown that the method is able to find and tracking community leaders in evolving networks. © 2020, Springer Nature Switzerland AG.

2019

Simplifying the Algorithm Selection Using Reduction of Rankings of Classification Algorithms

Authors
Abdulrahman, SM; Brazdil, P; Zainon, WMNW; Adamu, A;

Publication
2019 8TH INTERNATIONAL CONFERENCE ON SOFTWARE AND COMPUTER APPLICATIONS (ICSCA 2019)

Abstract
The average ranking method (AR) is one of the simplest and effective algorithms selection methods. This method uses metadata in the form of test results of a given set of algorithms on a given set of datasets and calculates an average rank for each algorithm. The ranks are used to construct the average ranking. In this paper we investigate the problem of how the rankings can be reduced by removing non-competitive and redundant algorithms, thereby reducing the number of tests a user needs to conduct on a new dataset to identify the most suitable algorithm. The method proposed involves two phases. In the first one, the aim is to identify the most competitive algorithms for each dataset used in the past. This is done with the recourse to a statistical test. The second phase involves a covering method whose aim is to reduce the algorithms by eliminating redundant variants. The proposed method differs from one earlier proposal in various aspects. One important one is that it takes both accuracy and time into consideration. The proposed method was compared to the baseline strategy which consists of executing all algorithms from the ranking. It is shown that the proposed method leads to much better performance than the baseline.

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)

Supervised
thesis

2017

Automatic Recommendation of Machine Learning Workflows

Author
Miguel Alexandre Viana Cachada

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

2017

Workflow Recommendation for Text Classification Problems

Author
Maria João Fernandes Ferreira

Institution
UP-FCUP

2017

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

Author
Salisu Mamman Abdulrahman

Institution
UP-FEP

2015

Development of a support system for workflow design for data mining problems that exploits Meta-learning

Author
Salisu Mamman Abdulrahman

Institution
UP-FEP