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Details

  • Name

    Carlos Manuel Soares
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    01st January 2008
008
Publications

2022

Meta-features for meta-learning

Authors
Rivolli, A; Garcia, LPF; Soares, C; Vanschoren, J; de Carvalho, ACPLF;

Publication
Knowl. Based Syst.

Abstract
Meta-learning is increasingly used to support the recommendation of machine learning algorithms and their configurations. These recommendations are made based on meta-data, consisting of performance evaluations of algorithms and characterizations on prior datasets. These characterizations, also called meta-features, describe properties of the data which are predictive for the performance of machine learning algorithms trained on them. Unfortunately, despite being used in many studies, meta-features are not uniformly described, organized and computed, making many empirical studies irreproducible and hard to compare. This paper aims to deal with this by systematizing and standardizing data characterization measures for classification datasets used in meta-learning. Moreover, it presents an extensive list of meta-features and characterization tools, which can be used as a guide for new practitioners. By identifying particularities and subtle issues related to the characterization measures, this survey points out possible future directions that the development of meta-features for meta-learning can assume. © 2022 Elsevier B.V.

2022

Multidimensional Subgroup Discovery on Event Logs

Authors
Ribeiro, J; Fontes, T; Soares, C; Borges, J;

Publication
SSRN Electronic Journal

Abstract

2021

Novelty Detection in Physical Activity

Authors
Leite, B; Abdalrahman, A; Castro, J; Frade, J; Moreira, J; Soares, C;

Publication
Proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021, Volume 2, Online Streaming, February 4-6, 2021.

Abstract

2021

Micro-MetaStream: Algorithm selection for time-changing data

Authors
Rossi, ALD; Soares, C; de Souza, BF; de Carvalho, ACPDF;

Publication
INFORMATION SCIENCES

Abstract
Data stream mining needs to deal with scenarios where data distribution can change over time. As a result, different learning algorithms can be more suitable in different time periods. This paper proposes micro-MetaStream, a meta-learning based method to recommend the most suitable learning algorithm for each new example arriving in a data stream. It is an evolution of MetaStream, which recommends learning algorithms for batches of examples. By using a unitary granularity, micro-MetaStream is able to respond more efficiently to changes in data distribution than its predecessor. The meta-data combines meta-features, characteristics describing recent data, with base-level features, the original variables of the new example. In experiments on real-world regression data streams, micro-metaStream outperformed MetaStream and a baseline method at the meta-level and frequently improved the predictive performance at the base-level.

2021

Preface

Authors
Soares, C; Torgo, L;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract

Supervised
thesis

2019

Learning to Rank with Random Forest: A Case Study in Hostel Reservations

Author
Carolina Macedo Moreira

Institution
UP-FEUP

2019

sistema de apoio à escolha de algoritmos para problemas de optimização

Author
Pedro Manuel Correia de Abreu

Institution
UP-FEUP

2019

Ensembles for Time Series Forecasting

Author
Vítor Manuel Araújo Cerqueira

Institution
UP-FEUP

2019

Prescriptive Analytics for Staff Scheduling Optimization in Retail

Author
Catarina Alexandra Teixeira Ramos

Institution
UP-FEUP

2019

Recommending Recommender Systems: tackling the Collaborative Filtering algorithm selection problem

Author
Tiago Daniel Sá Cunha

Institution
UP-FEUP