92 citations · 328 across the 17 of their papers we have counts for
17 papers
Multi-Label Transfer Learning in Non-Stationary Data Streams
Honghui Du, Leandro Minku, Aonghus Lawlor +1
Label concepts in multi-label data streams often experience drift in non-stationary environments, either independently or in relation to other labels. Transferring knowledge betwee…
MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments
Honghui Du, Leandro Minku, Huiyu Zhou
Concept drift is a major problem in online learning due to its impact on the predictive performance of data stream mining systems. Recent studies have started exploring data stream…
Cross-Modality Clustering-based Self-Labeling for Multimodal Data Classification
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 na…
SMOClust: Synthetic Minority Oversampling based on Stream Clustering for Evolving Data Streams
Chun Wai Chiu, Leandro L. Minku
Many real-world data stream applications not only suffer from concept drift but also class imbalance. Yet, very few existing studies investigated this joint challenge. Data difficu…
Knowledge Transfer for Dynamic Multi-objective Optimization with a Changing Number of Objectives
Gan Ruan, Leandro L. Minku, Stefan Menzel +2
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…
Evolutionary Optimization for Proactive and Dynamic Computing Resource Allocation in Open Radio Access Network
Gan Ruan, Leandro L. Minku, Zhao Xu +1
Intelligent techniques are urged to achieve automatic allocation of the computing resource in Open Radio Access Network (O-RAN), to save computing resource, increase utilization ra…
A Novel Generalised Meta-Heuristic Framework for Dynamic Capacitated Arc Routing Problems
Hao Tong, Leandro L. Minku, Stefan Menzel +2
The capacitated arc routing problem (CARP) is a challenging combinatorial optimisation problem abstracted from many real-world applications, such as waste collection, road gritting…
Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Model
Gustavo Oliveira, Leandro Minku, Adriano Oliveira
Real-world applications have been dealing with large amounts of data that arrive over time and generally present changes in their underlying joint probability distribution, i.e., c…
Metaheuristics "In the Large"
Jerry Swan, Steven Adriaensen, Alexander E. I. Brownlee +13
Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid…
Multi-Source Transfer Learning for Non-Stationary Environments
Honghui Du, Leandro L. Minku, Huiyu Zhou
In data stream mining, predictive models typically suffer drops in predictive performance due to concept drift. As enough data representing the new concept must be collected for th…
Better Software Analytics via "DUO": Data Mining Algorithms Using/Used-by Optimizers
Amritanshu Agrawal, Tim Menzies, Leandro L. Minku +2
This paper claims that a new field of empirical software engineering research and practice is emerging: data mining using/used-by optimizers for empirical studies or DUO. For examp…
A Systematic Study of Cross-Project Defect Prediction With Meta-Learning
Faimison Porto, Leandro Minku, Emilia Mendes +1
The prediction of defects in a target project based on data from external projects is called Cross-Project Defect Prediction (CPDP). Several methods have been proposed to improve t…
Data-Driven Search-based Software Engineering
Vivek Nair, Amritanshu Agrawal, Jianfeng Chen +6
This paper introduces Data-Driven Search-based Software Engineering (DSE), which combines insights from Mining Software Repositories (MSR) and Search-based Software Engineering (SB…
Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17)
Shuo Wang, Leandro L. Minku, Nitesh Chawla +1
With the wide application of machine learning algorithms to the real world, class imbalance and concept drift have become crucial learning issues. Class imbalance happens when the…
A Systematic Study of Online Class Imbalance Learning with Concept Drift
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. It deals with data streams having very skewe…
An empirical evaluation of ensemble adjustment methods for analogy-based effort estimation
Mohammad Azzeh, Ali Bou Nassif, Leandro L Minku
Objective: This paper investigates the potential of ensemble learning for variants of adjustment methods used in analogy-based effort estimation. The number k of analogies to be us…
The Handbook of Engineering Self-Aware and Self-Expressive Systems
Tao Chen, Funmilade Faniyi, Rami Bahsoon +4
When faced with the task of designing and implementing a new self-aware and self-expressive computing system, researchers and practitioners need a set of guidelines on how to use t…