Leandro L. Minku
Leandro L. Minku

Office UG39
School of Computer Science
University of Birmingham
Edgbaston
Birmingham, B15 2TT
UK

L.L.Minku ._at_. bham.ac.uk
+44 (0)121 414 6822

About Me
Projects
Publications
Teaching
My Lab
Open Positions
Open Source / Data
Other Activities

Publications

Copyright: The copyright of the papers below is owned by the respective publishers. Personal use of the electronic versions here provided is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the publishers.

Conference Papers

  1. ZYBLEWSKI, P. and MINKU, L. . "Cross-Modality Clustering-based Self-Labeling for Multimodal Data Classification", Workshop on Incremental Classification and Clustering, Concept Drift, Novelty Detection, Active Learning in Big/Fast Data Context (IncreLearn), in conjunction with the 24th IEEE International Conference on Data Mining (ICDM 2024), 2024.

  2. SHI, X.; MINKU, L.; YAO, X. . "Novel Memristive Reservoir Computing with Evolvable Topology for Time Series Prediction", 31st International Conference on Neural Information Processing (ICONIP'2024), 2024 (accepted). Paper also available here.

  3. SHI, X.; MINKU, L.; YAO, X. . "Tree-based Genetic Programming for Evolutionary Analog Circuit with Approximate Shapley Value". Forty-fourth SGAI International Conference on Artificial Intelligence (AI 2024), 2024 (accepted). Paper also available here.

  4. SONG, L.; MINKU, L.; TENG, C.; YAO, X. . "A Practical Human Labeling Method for Online Just-in-Time Software Defect Prediction". ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 2023. Paper also available here. Supplementary material available here.

  5. SHI, X.; WANG, Z.; MINKU, L.; YAO, X. . "Explaining Memristive Reservoir Computing Through Evolving Feature Attribution". Genetic and Evolutionary Computation Conference (GECCO) Companion, 4p. 2023. Paper available here.

  6. VERMETTEN, D.; VAN STEIN, B.; CARAFFINI, F.; MINKU, L.L.; KONONOVA, A.V. . "BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain", Genetic and Evolutionary Computation Conference (GECCO) Companion, Hot Off The Press paper, 2p, 2023. Paper avilable soon.

  7. TONG, H.; MINKU, L.; MENZEL, S.; SENDHOFF, B.; YAO, X. . "A Novel Generalized Metaheuristic Framework for Dynamic Capacitated Arc Routing Problems". Genetic and Evolutionary Computation Conference (GECCO) Companion, Hot Off The Press paper, 2p., 2023. Paper also available here.

  8. SAHA, S.; MINKU, L.L.; YAO, X.; SENDHOFF, B.; MENZEL, S. . "Split-AE: An Autoencoder-based Disentanglement Framework for 3D Shape-to-shape Feature Transfer", International Joint Conference on Neural Networks (IJCNN), July 2022 (accepted). Paper also available here.

  9. SONG, L.; LI, S.; MINKU, L.; YAO, X. . "A Novel Data Stream Learning Approach to Tackle One-Sided Label Noise From Verification Latency", International Joint Conference on Neural Networks (IJCNN), July 2022. Paper also available here.

  10. TONG, H.; MINKU, L.L.; MENZEL, D.; SENDHOFF, B.; YAO, X. . "Benchmarking Dynamic Capacitated Arc Routing Algorithms Using Real-World Traffic Simulation", IEEE Congress on Evolutionary Computation (CEC), July 2022 (accepted). Paper also available here.

  11. TONG, H.; MINKU, L.L.; MENZEL, D.; SENDHOFF, B.; YAO, X. . "What Makes The Dynamic Capacitated Arc Routing Problem Hard To Solve: Insights From Fitness Landscape Analysis", Genetic and Evolutionary Computation Conference (GECCO), 9p., July 2022. Paper available here.

  12. SHI, X.; GAO, J.; MINKU, L.L.; YAO, X. . "Evolving Parsimonious Circuits through Shapley Value-based Genetic Programming", Genetic and Evolutionary Computation Conference (GECCO), 4p., 2022. Paper available here.

  13. SAHA, S.; MINKU, L. L.; YAO, X.; SENDHOFF, B.; MENZEL, S. . "Exploiting 3D Variational Autoencoders For Interactive Vehicle Design", 17th International Design Conference (DESIGN), May 2022 (accepted). Paper also available here. Ranked in the top 10% papers according to reviewers' scores.

  14. SHI, X.; GAO, J.; MINKU, L. L.; YU, J. J. Q.; YAO, X. . "Second-order Time Delay Reservoir Computing for Nonlinear Time Series Problems", IEEE Symposium Series on Computational Intelligence (SSCI), December 2021 (accepted). Paper also available here

  15. SAHA, S.; RIOS, T.; MINKU, L.L.; VAS STEIN, B.; WOLLSTADT, P.; YAO, X.; BAECK, T.; SENDHOFF, B.; MENZEL, S. . "Exploiting Generative Models for Performance Predictions of 3D Car Designs", IEEE Symposium Series on Computational Intelligence (SSCI), December 2021 (accepted). Paper also available here

  16. MINKU, L.L. . "Multi-Stream Online Transfer Learning For Software Effort Estimation - Is It Necessary?", 17th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE), August 2021 (accepted). Paper also available here. Supplementary material available here. Source code available here.

  17. SAHA, S.; MINKU, L.; YAO, X.; SENDHOFF, B.; MENZEL, S. . "Exploiting Linear Interpolation of Variational Autoencoders for Satisfying Preferences in Evolutionary Design Optimization", IEEE Congress on Evolutionary Computation, June 2021. Paper also available here.

  18. TONG, H.; MENZEL, S.; YAO, X. . "A Hybrid Local Search Framework for the Dynamic Capacitated Arc Routing Problem", Genetic and Evolutionary Computation Conference (GECCO), 2 pages, July 2021 (accepted). Paper also available here.

  19. SAHA, S.; MENZEL, S.; MINKU, L.; YAO, X.; SENDHOFF, B.; WOLLSTADT, P. . "Quantifying The Generative Capabilities Of Variational Autoencoders For 3D Car Point Clouds", 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 9 pages, December 2020. Paper also available here.

  20. DU, H.; MINKU, L.; ZHOU, H. . "MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments", 20th IEEE International Conference on Data Mining (ICDM), 10 pages, November 2020. Paper also available here. Supplementary material here. (Acceptance rate: 9.8%)

  21. TONG, H.; MINKU, L.; MENZEL, S.; SENDHOFF, B.; YAO, X. . "Towards Novel Meta-heuristic Algorithms for Dynamic Capacitated Arc Routing Problems", Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN XVI), p. 428--440, September 2020. Paper also available here.

  22. WANG, S.; MINKU, L. . "AUC Estimation and Concept Drift Detection for Imbalanced Data Streams with Multiple Classes", IEEE International Joint Conference on Neural Networks (IJCNN), 8 pages, July 2020. Paper also available here.

  23. RUAN, G.; MINKU, L.L.; MENZEL, S.; SENDHOFF, B.; YAO, X. . "Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization", 2020 IEEE International Conference on Evolutionary Computation (CEC), July 2020. Paper also available here. (best student paper finalist)

  24. TABASSUM, S.; MINKU, L.L.; FENG, D.; CABRAL, G.; SONG, L. . "An Investigation of Cross-Project Learning in Online Just-In-Time Software Defect Prediction", 2020 International Conference on Software Engineering (ICSE), p. 554-565, June 2020, doi: 10.1145/3377811.3380403. Paper also available here. (Please note typo in a formula as highlighted by a comment in the pdf.) Supplementary material here. (Acceptance rate: 20.9%)

  25. SAHA, S.; RIOS, T.; MINKU, L.; YAO, X.; XU, Z.; SENDHOFF, B.; MENZEL, S. . "Optimal Evolutionary Optimization Hyper-parameters to Mimic Human User Behavior", 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 9 pages, December 2019, doi: 10.1109/SSCI44817.2019.9002958. Paper also available here.

  26. RUAN, G.; MINKU, L.; MENZEL, S.; SENDHOFF, B.; YAO, X. . "When and How to Transfer Knowledge in Dynamic Multi-Objective Optimization", 2019 IEEE Symposium Series on Computational Intelligence (SSCI), December 2019, doi: 10.1109/SSCI44817.2019.9002815. Paper also available here.

  27. DU, H.; MINKU, L. L.; ZHOU, H. . "Multi-Source Transfer Learning for Non-Stationary Environments", Proceedings of the International Joint Conference on Neural Networks (IJCNN), July 2019. DOI: 10.1109/IJCNN.2019.8852024. Paper also available here. Source code and data sets available here

  28. OLIVEIRA, G. H. F. M.; MINKU, L. L.; OLIVEIRA, A. L. I. . "GMM-VRD: A Gaussian Mixture Model for Dealing With Virtual and Real Concept Drifts", Proceedings of the International Joint Conference on Neural Networks (IJCNN), 8 pages, July 2019. Paper also available here. Source code available here.

  29. CABRAL, G.; MINKU, L.; SHIHAB, E.; MUJAHID, S. . "Class Imbalance Evolution and Verification Latency in Just-in-Time Software Defect Prediction", Proceedings of the International Conference on Software Engineering (ICSE), p. 666-676, May 2019. Paper also available here. Presentation available here. (Acceptance rate: 21%)

  30. SHEHU, B.; HECKEL, R.; MINKU, L. . "Reverse Engineering the Behaviour of Twitter Bots", Proceedings of the 5th Int Conference on Internet of Things, Systems, Management & Security (IOTSMS 2018), 8 pages, October 2018.

  31. SONG, L.; MINKU, L.L.; YAO, X. . "A Novel Automated Approach for Software Effort Estimation Based on Data Augmentation", ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), p. 468–479, November 2018. Paper also available here. Supplementary material available here. Presentation available . (Paper unconditionally accepted -- acceptance rate for unconditionally accepted papers: 19%. Acceptance rate including conditionally accepted papers: 21%.)

  32. WALKINSHAW, N.; MINKU, L. . "Are 20% of Files Responsible for 80% of Defects?", Proceedings of the 9th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), p. 2.1:2.10, October 2018. Paper also available here. (Acceptance rate considering all submissions: 18%. Acceptance rate excluding desk rejected papers: 21%. Paper features at IEEE Software's Practitioner Digest)

  33. CHIU, C.W.; MINKU, L.L. . "Diversity-Based Pool of Models for Dealing with Recurring Concepts", International Joint Conference on Neural Networks, p. 2759-2766, July 2018. Paper also available here.

  34. NAIR, V.; AGRAWAL, A.; CHEN, J.; FU, W.; MATHEW, G.; MENZIES, L.; MINKU, L; WAGNER, M.; YU, Z. . "Data-Driven Search-Based Software Engineering", International Conference on Mining Software Repositories (MSR), p. 341--352, May 2018. Paper also available at arXiv.

  35. OLIVEIRA, G.H.F.M.; CAVALCANTE, R.C.; CABRAL, G.G.; MINKU, L.L.; OLIVEIRA, A.L.I.. "Time Series Forecasting in the Presence of Concept Drift: A PSO-based Approach", IEEE International Conference on Tools with Artificial Intelligence (ICTAI), November 2017. Paper also available here. Source code available here.

  36. MINKU, L.; HOU, S.. "Clustering Dycom: An Online Cross-Company Software Effort Estimation Study", Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE), November 2017. Paper also available here.

  37. HUIJGENS, H.; CAN DEURSEN, A.; MINKU, L.; LOKAN, C. . "Effort and Cost of Software Engineering: A Comparison of Two Industrial Data Sets", Proceedings of the Evaluation and Assessment in Software Engineering Conference (EASE), June 2017. Paper also available here.

  38. MINKU, L. "On the Terms Within- and Cross-Company in Software Effort Estimation", Proceedings of the 12th International Conference on Predictive Models and Data Analytics in Software Engineering, September 2016. DOI: 10.1145/2972958.2972968. Paper also available here.

  39. SOBHY, D.; BAHSOON, R.; MINKU, L.; KAZMAN, R. "Diversifying Software Architecture for Sustainability: A Value-based Perspective", Proceedings of the 10th European Conference on Software Architecture (ECSA), November 2016. Paper available here.

  40. WU, X.; CONSOLI, P.; MINKU, L.L.; OCHOA, G.; YAO, X. "An Evolutionary Hyper-Heuristic for the Software Project Scheduling Problem", Proceedings of the 14th International Conference on Parallel Problem Solving from Nature (PPSN'16), September 2016. Paper available here.

  41. WANG, S.; MINKU, L.L.; YAO, X. "Dealing with Multiple Classes in Online Class Imbalance Learning", Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16), p. 2118--2124, July 2016. Paper available here. (acceptance rate: 24%)

  42. CAVALCANTE, R.; MINKU, L.L.; OLIVEIRA, A. "FEDD: Feature Extraction for Explicit Concept Drift Detection in Time Series" Proceedings of the 2016 IEEE International Joing Conference on Neural Networks (IJCNN'16), July 2016. Paper also available here.

  43. MINKU, L.L.; SARRO, F.; MENDES, E.; FERRUCCI, F.; "How to Make Best Use of Cross-Company Data for Web Effort Estimation?" Proceedings of the 9th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM'15), p. 172--181, October 2015 (best paper award; acceptance rate 25%). Paper also available here and here.

  44. DU, X.; NI, Y.; YE, P.; YAO, X.; MINKU, L.; XIAO, R.; "An Evolutionary Algorithm for Performance Optimization at Software Architecture Level", Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC'15), 8p., May 2015. Paper also available here.

  45. MURWANTARA, I M.; BORDBAR, B.; MINKU, L. L.; "Measuring Energy Consumption for Web Service Product Configuration", Proceedings of the 16th International Conference on Information Integration and Web-based Applications & Services (iiWAS'14), p. 224--228, December 2014, doi 10.1145/2684200.2684314 . Paper also available here.

  46. CONSOLI, P; MINKU, L. L.; YAO, X.; "Dynamic Selection of Evolutionary Algorithm Operators Based on Online Learning and Fitness Landscape Metrics", Proceedings of the 10th International Conference on Simulated Evolution And Learning (SEAL'14), Lecture Notes in Computer Science Volume 8886, p. 359--370, December 2014, doi 10.1007/978-3-319-13563-2_31. Paper also available here.

  47. SONG, L; MINKU, L. L.; YAO, X.; "The Potential Benefit of Relevance Vector Machine to Software Effort Estimation", Proceedings of the 10th International Conference on Predictive Models in Software Engineering (PROMISE'14), p. 52--61, September 2014. Paper available via ACM open access here.

  48. HARMAN, M.; ISLAM, S.; JIA, Y.; MINKU, L.; SARRO, F.; SRIVISUT, K.; "Less is More: Temporal fault predictive performance over multiple Hadoop releases", Symposium on Search-Based Software Engineering (SSBSE'14), Lecture Notes in Computer Science Volume 8636, p. 240-246, August 2014. Paper also available here.

  49. MINKU, L. L.; YAO, X.; "How to Make Best Use of Cross-company Data in Software Effort Estimation?", Proceedings of the 36th International Conference on Software Engineering (ICSE'14), p. 446-456, May 2014, doi: 10.1145/2568225.2568228. Paper available via ACM open access here. Paper also available here. NOTE: Typo in table 2 (replace 5.00 by 5.60 in the productivity band of ISBSG2000 and 10.00 by 10.10 in the productivity band of ISBSG). Typo in number of ISBSG WC projects: should be 184 instead of 187. Presentation available here.(acceptance rate: 20%)

  50. WANG, S.; MINKU, L. L.; YAO, X.; "A Multi-Objective Ensemble Method for Online Class Imbalance Learning", Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN'14), p. 3311-3318p., 2014.

  51. SONG, L.; MINKU, L. L.; YAO, X.; "The Impact of Parameter Tuning on Software Effort Estimation Using Learning Machines", Proceedings of the 9th International Conference on Predictive Models in Software Engineering (PROMISE'13), 10p., October 2013, doi: 10.1145/2499393.2499394. Paper also available here.

  52. MINKU, L. L.; YAO, X.; "An Analysis of Multi-objective Evolutionary Algorithms for Training Ensemble Models Based on Different Performance Measures in Software Effort Estimation", Proceedings of the 9th International Conference on Predictive Models in Software Engineering (PROMISE'13), 10p., October 2013, doi: 10.1145/2499393.2499396. Paper also available here. Presentation available here. Link to preprocessed PROMISE data sets used in the study here.

  53. WANG, S.; MINKU, L.; GHEZZI, D.; CALTABIANO, D.; TINO, P.; YAO, X. . "Concept Drift Detection for Online Class Imbalance Learning", Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN), 10 pages, August 2013, doi: 10.1109/IJCNN.2013.6706768. Paper also available here.

  54. WANG, S.; MINKU, L.; YAO, X. . "A Learning Framework for Online Class Imbalance Learning", IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL), at IEEE Symposium Series on Computational Intelligence (SSCI), p. 36-45, Singapore, April 2013, doi: 10.1109/CIEL.2013.6613138, (best paper nomination). Paper also available here.

  55. MINKU, L.; YAO, X. . "Can Cross-company Data Improve Performance in Software Effort Estimation?", Proceedings of the 8th International Conference on Predictive Models in Software Engineering (PROMISE'2012), p. 69-78, Lund, Sweden, September 2012, doi: 10.1145/2365324.2365334. Paper also available here. NOTE: Typo in table 1 (replace 5 by 5.6 in the productivity band of ISBSG2000 and 10 by 10.1 in the productivity band of ISBSG). Typo in numberof ISBSG WC projects: should be184 instead of 184.

  56. MINKU, L.; SUDHOLT, D.; YAO, X. . "Evolutionary algorithms for the project scheduling problem: runtime analysis and improved design", Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'2012), p. 1221-1228, Philadelphia, July 2012, doi: 10.1145/2330163.2330332 (best paper nomination). Paper available here (ACM Author-Izer). Paper also available here.

  57. MINKU, L.; YAO, X. . "Using Unreliable Data for Creating More Reliable Online Learners", Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN'2012), p. 2492-2499, Brisbane, Australia, June 2012, doi: 10.1109/IJCNN.2012.6252711. Paper also available here.

  58. MINKU, L.; YAO, X. . "A Principled Evaluation of Ensembles of Learning Machines for Software Effort Estimation", Proceedings of the 7th International Conference on Predictive Models in Software Engineering (PROMISE'2011), 10p., Banff, Canada, September 2011, doi: 10.1145/2020390.2020399 (Selected as one of the three best papers). Paper available here. (ACM Author-Izer). Paper also available here. Presentation available here. Link to preprocessed PROMISE data sets used in the study here.

  59. MINKU, L.; YAO, X. . "Using Diversity to Handle Concept Drift in On-line Learning",Proceedings of the International Joint Conference on Neural Networks (IJCNN'2009),p. 2125-2132, Atlanta, June 2009, doi: 10.1109/IJCNN.2009.5179008.

  60. MINKU, L.; YAO, X. . "On-line Bagging Negative Correlation Learning", Proceedings of the International Joint Conference on Neural Networks (IJCNN'2008), Part III, p. 1375-1382, Hong Kong, June 2008, doi: 10.1109/IJCNN.2008.4633977. Paper also available here

  61. MINKU, L.; LUDERMIR, T. B. . "EFuNN Ensembles Construction Using a Clustering Method and a Coevolutionary Multi-Objective Genetic Algorithm", Proceedings of the 3th International Conference on Neural Information Processing (ICONIP'2006), Part III, Lecture Notes in Computer Science 4234, p. 884-891, Hong Kong, October 2006, doi: 10.1007/11893295_97. Paper also available here.

  62. MINKU, L.; LUDERMIR, T. B. . "EFuNN Ensembles Construction Using CONE with Multi-objective GA", Proceedings of the IX Brazilian Neural Networks Symposium (SBRN'2006) p. 48-53, Ribeirao Preto, Brazil, October 2006, doi: 10.1109/SBRN.2006.16. Paper also available here.

  63. MINKU, L.; LUDERMIR, T. B. . "EFuNNs Ensembles Construction Using a Clustering Method and a Coevolutionary Genetic Algorithm", Proceedings of the 2006 IEEE Congress on Evolutionary Computation (CEC'2006), p. 1399-1406, Vancouver, Canada, July 2006, doi: 10.1109/CEC.2006.1688472. Paper also available here

  64. ZANCHETTIN, C.; MINKU, L.; LUDERMIR, T. B. . "Design of Experiments in Neuro-Fuzzy Systems", Proceedings of the 5th International Conference on Hybrid Intelligent Systems (HIS'2005) 6p., Rio de Janeiro, Brazil, November 2005, doi: 10.1109/ICHIS.2005.34. Extended IJCIA version here.

  65. MINKU, L.; LUDERMIR, T. B. . "Estrategia Evolucionaria e Algoritmos Geneticos para Otimizacao Dinamica de Parametros de EFuNNs.", VII Congresso Brasileiro de Redes Neurais (CBRN'2005), 6p., Natal, Rio Grande do Norte, Brazil, October 2005. (in Portuguese)

  66. MINKU, L.; LUDERMIR, T. B. . "Evolutionary Strategies and Genetic Algorithms for Dynamic Parameter Optimization of Evolving Fuzzy Neural Networks", Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC'2005), v. 3, p. 1951-1958, Edinburgh, Scotland, September 2005, doi: 10.1109/CEC.2005.1554934. Paper also available here.

  67. MINKU, L.; LUDERMIR, T. B.; ARAUJO, A. F. R. . "Computacao Evolucionaria para otimizacao dinamica de parametros de EFuNNs", In: V Encontro Nacional de Inteligencia Artificial (ENIA'2005), p. 612-621, Sao Leopoldo, Rio Grande do Sul, Brazil, July 2005. Paper also available here. (in Portuguese)