Publications

Publications by LIAAD

2026

Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part I

Authors
Ribeiro, RP; Pfahringer, B; Japkowicz, N; Larrañaga, P; Jorge, AM; Soares, C; Abreu, PH; Gama, J;

Publication
ECML/PKDD (1)

Abstract

2026

Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part IX

Authors
Dutra, I; Pechenizkiy, M; Cortez, P; Pashami, S; Jorge, AM; Soares, C; Abreu, PH; Gama, J;

Publication
ECML/PKDD (9)

Abstract

2026

Machine Learning and Knowledge Discovery in Databases. Research Track and Applied Data Science Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part VIII

Authors
Pfahringer, B; Japkowicz, N; Larrañaga, P; Ribeiro, RP; Dutra, I; Pechenizkiy, M; Cortez, P; Pashami, S; Jorge, AM; Soares, C; Abreu, PH; Gama, J;

Publication
ECML/PKDD (8)

Abstract

2026

Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part X

Authors
Dutra, I; Pechenizkiy, M; Cortez, P; Pashami, S; Pasquali, A; Moniz, N; Jorge, AM; Soares, C; Abreu, PH; Gama, J;

Publication
ECML/PKDD (10)

Abstract

2026

Preface

Authors
Ribeiro, P; Japkowicz, N; Jorge, AM; Soares, C; Abreu, PH; Pfahringer, B; Gama, MP; Larrañaga, P; Dutra, I; Pechenizkiy, M; Pashami, S; Cortez, P;

Publication
Lecture Notes in Computer Science

Abstract
[No abstract available]

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2026

Fish swarm parameter self-tuning for data streams

Authors
Veloso, BM; Neto, HA; Buarque, F; Gama, MP;

Publication
Data Mining and Knowledge Discovery

Abstract
Hyper-parameter optimization in machine learning models is critical for achieving peak performance. Over the past few years, numerous researchers have worked on this optimization challenge. They primarily focused on batch learning tasks where data distributions remain relatively unchanged. However, addressing the properties of data streams poses a substantial challenge. With the rapid evolution of technology, the demand for sophisticated techniques to handle dynamic data streams is becoming increasingly urgent. This paper introduces a novel adaptation of the Fish School Search (FSS) Algorithm for online hyper-parameter optimization, the FSS-SPT. The FSS-SPT is a solution designed explicitly for the dynamic context of data streams. One fundamental property of the FSS-SPT is that it can change between exploration and exploitation modes to cope with the concept drift and converge to reasonable solutions. Our experiments on different datasets provide compelling evidence of the superior performance of our proposed methodology, the FSS-SPT. It outperformed existing algorithms in two machine learning tasks, demonstrating its potential for practical application. © The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2025.

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