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Computer Science

D-Index
37
Citations
6784
World Ranking
10632
National Ranking
668

Overview

Leandro L. Minku is affiliated with the University of Birmingham in the United Kingdom and specializes in the field of Computer Science, with a particular focus on Artificial Intelligence, Industrial and Manufacturing Engineering, Computer Networks and Communications, Information Systems, and Electrical and Electronic Engineering.

Their research covers diverse topics including:

  • Data Stream Mining Techniques
  • Software Engineering Research
  • Software System Performance and Reliability
  • Machine Learning and Data Classification
  • Software Reliability and Analysis Research
  • Advanced Multi-Objective Optimization Algorithms
  • Metaheuristic Optimization Algorithms Research

Recent publications by Leandro L. Minku include:

  • Surrogate models in evolutionary single-objective optimization: A new taxonomy and experimental study (2021), published in Information Sciences
  • Metaheuristics "In the Large" (2021), published in European Journal of Operational Research
  • Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Model Approach (2021), published in IEEE Transactions on Knowledge and Data Engineering
  • The impact of data difficulty factors on classification of imbalanced and concept drifting data streams (2021), published in Knowledge and Information Systems
  • A Diversity Framework for Dealing With Multiple Types of Concept Drift Based on Clustering in the Model Space (2020), published in IEEE Transactions on Neural Networks and Learning Systems

Frequent co-authors collaborating with Minku include:

  • Xin Yao
  • Stefan Menzel
  • Bernhard Sendhoff
  • Liyan Song
  • Gan Ruan

Minku's work has been published extensively in a range of venues, notably:

  • IEEE Computational Intelligence Magazine, with 18 publications
  • arXiv (Cornell University), 7 publications
  • Zenodo (CERN European Organization for Nuclear Research), 6 publications
  • Empirical Software Engineering, 5 publications
  • IEEE Transactions on Neural Networks and Learning Systems, 4 publications

In addition to journal and conference papers, Minku has contributed to book publications, with recorded work published by the European Organization for Nuclear Research, including the volume titled ieee-cis/IEEE-CIS-Open-Access-Book-Volume-1: FirstEdition in 2023.

Best Publications

  • Ensemble learning for data stream analysis

    Bartosz Krawczyk;Leandro L. Minku;Joo Gama;Jerzy Stefanowski

  • The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift

    L.L. Minku;A.P. White;Xin Yao

  • DDD: A New Ensemble Approach for Dealing with Concept Drift

    L. L. Minku;Xin Yao

  • Resampling-Based Ensemble Methods for Online Class Imbalance Learning

    Shuo Wang;Leandro L. Minku;Xin Yao

  • A Systematic Study of Online Class Imbalance Learning With Concept Drift

    Shuo Wang;Leandro L. Minku;Xin Yao

  • Ensembles and locality

    Leandro L. Minku;Xin Yao

  • Online Ensemble Learning of Data Streams with Gradually Evolved Classes

    Yu Sun;Ke Tang;Leandro L. Minku;Shuo Wang

  • A learning framework for online class imbalance learning

    Shuo Wang;Leandro L. Minku;Xin Yao

  • Software effort estimation as a multiobjective learning problem

    Leandro L. Minku;Xin Yao

  • Next challenges for adaptive learning systems

    Indre Zliobaite;Albert Bifet;Mohamed Gaber;Bogdan Gabrys

  • A Q-learning-based memetic algorithm for multi-objective dynamic software project scheduling

    Xiao-Ning Shen;Leandro L. Minku;Naresh Marturi;Yi-Nan Guo

  • Concept drift detection for online class imbalance learning

    Shuo Wang;Leandro L. Minku;Davide Ghezzi;Daniele Caltabiano

  • An empirical evaluation of ensemble adjustment methods for analogy-based effort estimation

    Mohammad Azzeh;Ali Bou Nassif;Leandro L. Minku

  • Surrogate models in evolutionary single-objective optimization: A new taxonomy and experimental study

    Hao Tong;Changwu Huang;Leandro L. Minku;Xin Yao

  • The impact of parameter tuning on software effort estimation using learning machines

    Liyan Song;Leandro L. Minku;Xin Yao

  • Class imbalance evolution and verification latency in just-in-time software defect prediction

    George G. Cabral;Leandro L. Minku;Emad Shihab;Suhaib Mujahid

  • ONLINE CLASS IMBALANCE LEARNING AND ITS APPLICATIONS IN FAULT DETECTION

    Shuo Wang;Leandro L. Minku;Xin Yao

  • Metaheuristics “In the Large”

    Jerry Swan;Steven Adriaensen;Alexander E.I. Brownlee;Kevin Hammond

  • How to make best use of cross-company data in software effort estimation?

    Leandro L. Minku;Xin Yao

  • Dynamic Software Project Scheduling through a Proactive-Rescheduling Method

    Xiaoning Shen;Leandro L. Minku;Rami Bahsoon;Xin Yao

  • Sharing Data and Models in Software Engineering: Sharing Data and Models

    Tim Menzies;Ekrem Kocaguneli;Leandro L. Minku;Fayola Peters

Frequent Co-Authors

Xin Yao
Xin Yao Lingnan University
Burak Turhan
Burak Turhan Monash University
Tim Menzies
Tim Menzies North Carolina State University
Ayse Bener
Ayse Bener Toronto Metropolitan University
Bernhard Sendhoff
Bernhard Sendhoff Honda (Germany)
Emilia Mendes
Emilia Mendes Aarhus University
Markus Wagner
Markus Wagner Monash University
Nitesh V. Chawla
Nitesh V. Chawla University of Notre Dame
Rick Kazman
Rick Kazman University of Hawaii at Manoa
Huiyu Zhou
Huiyu Zhou University of Leicester

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