Portrait of Vladimir Makarenkov

Vladimir Makarenkov

Affiliate Member
Full Professor, UQAM, Department of Computer Science
Research Topics
Clustering
Computational Biology
Deep Learning
Medical Machine Learning
Website
Google Scholar

Biography

Vladimir Makarenkov is a full professor and director of the graduate program in bioinformatics at Université du Québec à Montréal (UQAM). He holds a master's degree in applied mathematics from Lomonosov Moscow State University and a PhD in computer science and mathematics from the École des hautes études en sciences sociales (EHESS) in Paris. Before joining the computer science department at UQAM, he completed a three-year postdoctoral fellowship at the Digital Ecology Lab at Université de Montréal.

He is the author of 80 journal articles and 67 conference papers, and the recipient of the prestigious Simon Régnier Prize and the Chikio Hayashi Prize awarded by the International Society for Mathematical Classification. His research focuses on AI, bioinformatics and data mining. This encompasses the design and development of novel unsupervised and supervised machine learning methods, as well as the use of machine learning techniques, including clustering and deep learning, for the analysis of biological and biomedical data.

Makarenkov’s current research also involves the development of an automated recommendation system based on deep learning to recommend the best clustering algorithm for a given input dataset. Additionally, he is working on creating a generic machine learning model to define the concept of cluster, and on comparing various auto-encoding approaches and clustering algorithms to achieve better clustering results.

Publications

Improving clustering quality evaluation in noisy Gaussian mixtures
Renato Cordeiro De Amorim
Vladimir Makarenkov
2025-03-01
ArXiv (preprint)
arxiv.org
arxiv.org
Improving internal cluster quality evaluation in noisy Gaussian mixtures
Renato Cordeiro De Amorim
Vladimir Makarenkov
Clustering is a fundamental technique in machine learning and data analysis, widely used across various domains. Internal clustering validat… (see more)ion measures, such as the Average Silhouette Width, Calinski-Harabasz, and Davies-Bouldin indices, play a crucial role in assessing clustering quality when external ground truth labels are unavailable. However, these measures can be affected by feature relevance, potentially leading to unreliable evaluations in high-dimensional or noisy data sets. In this paper, we introduce a Feature Importance Rescaling (FIR) method designed to enhance internal clustering validation by adjusting feature contributions based on their dispersion. Our method systematically attenuates noise features making clustering compactness and separation clearer, and by consequence aligning internal validation measures more closely with the ground truth. Through extensive experiments on synthetic data sets under different configurations, we demonstrate that FIR consistently improves the correlation between internal validation indices and the ground truth, particularly in settings with noisy or irrelevant features. The results show that FIR increases the robustness of clustering evaluation, reduces variability in performance across different data sets, and remains effective even when clusters exhibit significant overlap. These findings highlight the potential of FIR as a valuable enhancement for internal clustering validation, making it a practical tool for unsupervised learning tasks where labelled data is not available.
2025-03-01
ArXiv (preprint)
arxiv.org
arxiv.org
Improving internal cluster quality evaluation in noisy Gaussian mixtures
Renato Cordeiro De Amorim
Vladimir Makarenkov
Clustering is a fundamental technique in machine learning and data analysis, widely used across various domains. Internal clustering validat… (see more)ion measures, such as the Average Silhouette Width, Calinski-Harabasz, and Davies-Bouldin indices, play a crucial role in assessing clustering quality when external ground truth labels are unavailable. However, these measures can be affected by feature relevance, potentially leading to unreliable evaluations in high-dimensional or noisy data sets. In this paper, we introduce a Feature Importance Rescaling (FIR) method designed to enhance internal clustering validation by adjusting feature contributions based on their dispersion. Our method systematically attenuates noise features making clustering compactness and separation clearer, and by consequence aligning internal validation measures more closely with the ground truth. Through extensive experiments on synthetic data sets under different configurations, we demonstrate that FIR consistently improves the correlation between internal validation indices and the ground truth, particularly in settings with noisy or irrelevant features. The results show that FIR increases the robustness of clustering evaluation, reduces variability in performance across different data sets, and remains effective even when clusters exhibit significant overlap. These findings highlight the potential of FIR as a valuable enhancement for internal clustering validation, making it a practical tool for unsupervised learning tasks where labelled data is not available.
2025-03-01
ArXiv (preprint)
arxiv.org
arxiv.org
BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averaging
Zeinab Sherkatghanad
Moloud Abdar
Mohammadreza Bakhtyari
Vladimir Makarenkov
Test-time augmentation (TTA) is a well-known technique employed during the testing phase of computer vision tasks. It involves aggregating m… (see more)ultiple augmented versions of input data. Combining predictions using a simple average formulation is a common and straightforward approach after performing TTA. This paper introduces a novel framework for optimizing TTA, called BayTTA (Bayesian-based TTA), which is based on Bayesian Model Averaging (BMA). First, we generate a model list associated with different variations of the input data created through TTA. Then, we use BMA to combine model predictions weighted by their respective posterior probabilities. Such an approach allows one to take into account model uncertainty, and thus to enhance the predictive performance of the related machine learning or deep learning model. We evaluate the performance of BayTTA on various public data, including three medical image datasets comprising skin cancer, breast cancer, and chest X-ray images and two well-known gene editing datasets, CRISPOR and GUIDE-seq. Our experimental results indicate that BayTTA can be effectively integrated into state-of-the-art deep learning models used in medical image analysis as well as into some popular pre-trained CNN models such as VGG-16, MobileNetV2, DenseNet201, ResNet152V2, and InceptionRes-NetV2, leading to the enhancement in their accuracy and robustness performance.
2024-06-25
ArXiv (preprint)
doi.org
arxiv.org
A self-attention-based CNN-Bi-LSTM model for accurate state-of-charge estimation of lithium-ion batteries
Zeinab Sherkatghanad
Amin Ghazanfari
Vladimir Makarenkov
2024-05-01
Journal of Energy Storage (published)
doi.org
A self-attention-based CNN-Bi-LSTM model for accurate state-of-charge estimation of lithium-ion batteries
Zeinab Sherkatghanad
Amin Ghazanfari
Vladimir Makarenkov
2024-05-01
Journal of Energy Storage (published)
doi.org
A self-attention-based CNN-Bi-LSTM model for accurate state-of-charge estimation of lithium-ion batteries
Zeinab Sherkatghanad
Amin Ghazanfari
Vladimir Makarenkov
2024-05-01
Journal of Energy Storage (published)
doi.org
A self-attention-based CNN-Bi-LSTM model for accurate state-of-charge estimation of lithium-ion batteries
Zeinab Sherkatghanad
Amin Ghazanfari
Vladimir Makarenkov
2024-05-01
Journal of Energy Storage (published)
doi.org
Assessing the emergence time of SARS-CoV-2 zoonotic spillover
Stéphane Samson
Étienne Lord
Vladimir Makarenkov
2024-04-04
PLoS ONE (published)
doi.org
Inertia-Based Indices to Determine the Number of Clusters in K-Means: An Experimental Evaluation
Andrei Rykov
Renato Cordeiro De Amorim
Vladimir Makarenkov
Boris Mirkin
This paper gives an experimentally supported review and comparison of several indices based on the conventional K-means inertia criterion fo… (see more)r determining the number of clusters,
2024-01-01
IEEE Access (published)
doi.org
Inertia-Based Indices to Determine the Number of Clusters in K-Means: An Experimental Evaluation
Andrei Rykov
Renato Cordeiro De Amorim
Vladimir Makarenkov
Boris Mirkin
This paper gives an experimentally supported review and comparison of several indices based on the conventional K-means inertia criterion fo… (see more)r determining the number of clusters,
2024-01-01
IEEE Access (published)
doi.org
Inertia-Based Indices to Determine the Number of Clusters in K-Means: An Experimental Evaluation
Andrei Rykov
Renato Cordeiro De Amorim
Vladimir Makarenkov
Boris Mirkin
This paper gives an experimentally supported review and comparison of several indices based on the conventional K-means inertia criterion fo… (see more)r determining the number of clusters,
2024-01-01
IEEE Access (published)
doi.org