no code implementations • 5 Aug 2024 • Paweł Zyblewski, Leandro L. Minku
Technological advances facilitate the ability to acquire multimodal data, posing a challenge for recognition systems while also providing an opportunity to use the heterogeneous nature of the information to increase the generalization capability of models.
1 code implementation • 28 Aug 2023 • Chun Wai Chiu, Leandro L. Minku
Based on the compressed information, synthetic examples can be created within the region that recently generated new minority class examples.
no code implementations • 19 Jun 2023 • Gan Ruan, Leandro L. Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao
Different from most other dynamic multi-objective optimization problems (DMOPs), DMOPs with a changing number of objectives usually result in expansion or contraction of the Pareto front or Pareto set manifold.
no code implementations • 12 Jan 2022 • Gan Ruan, Leandro L. Minku, Zhao Xu, Xin Yao
However, the existing problem formulation to solve this resource allocation problem is unsuitable as it defines the capacity utility of resource in an inappropriate way and tends to cause much delay.
1 code implementation • 14 Apr 2021 • Hao Tong, Leandro L. Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao
The few existing studies are limited by the dynamic scenarios considered, and by overly complicated algorithms that are unable to benefit from the wealth of contributions provided by the existing CARP literature.
no code implementations • 19 Nov 2020 • Jerry Swan, Steven Adriaensen, Alexander E. I. Brownlee, Kevin Hammond, Colin G. Johnson, Ahmed Kheiri, Faustyna Krawiec, J. J. Merelo, Leandro L. Minku, Ender Özcan, Gisele L. Pappa, Pablo García-Sánchez, Kenneth Sörensen, Stefan Voß, Markus Wagner, David R. White
We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the field, easing the exploration of the design space of metaheuristics.
1 code implementation • 7 Jan 2019 • Honghui Du, Leandro L. Minku, Huiyu Zhou
To speed up recovery from concept drift and improve predictive performance in data stream mining, this work proposes a novel approach called Multi-sourcE onLine TrAnsfer learning for Non-statIonary Environments (Melanie).
no code implementations • 28 Jul 2017 • Shuo Wang, Leandro L. Minku, Nitesh Chawla, Xin Yao
It provides a forum for international researchers and practitioners to share and discuss their original work on addressing new challenges and research issues in class imbalance learning, concept drift, and the combined issues of class imbalance and concept drift.
no code implementations • 20 Mar 2017 • Shuo Wang, Leandro L. Minku, Xin Yao
As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift.