Published Research

Articles

• Payel Sadhukhan, Samrat Gupta; A Graph Theoretic Approach to Assess Quality of Data for Classification Task, Data and Knowledge Engineering 2025

• Payel Sadhukhan, Sarbani Palit: Natural-neighborhood based, label-specific undersampling for imbalanced, multi-label data, Advances in Data Analysis and Classification 2024.

• Shion Sammader Chaudhury, Payel Sadhukhan, Kausik Sengupta. Explainable AI using the Wasserstein Distance Approach. IEEE Access 2024.

• Payel Sadhukhan, Labani Halder, Sarbani Palit: Approximate DBSCAN on obfuscated data, Journal of Information Security and Applications, 2024.

• Payel Sadhukhan, Sarbani Palit. Oversampling the minority class using a dedicated fitness function and genetic algorithmic progression. Concurrency Computation, Practice and Experience 2021.

• Payel Sadhukhan,, Sarbani Palit. Multi-label learning on Principles of Reverse Nearest Neighbourhood. Expert Systems. 2021.

• Payel Sadhukhan,, Sarbani Palit: Adaptive Learning of Minority Class prior to Minority Oversampling. Pattern Recognition Letters. 2020.

• Payel Sadhukhan,, Sarbani Palit. Lattice and Imbalance Informed Multi-label Learning. IEEE Access. 2020.

• Payel Sadhukhan,. Can Reverse Nearest Neighbors perceive unknowns? IEEE Access. 2020.

• Payel Sadhukhan,, Sarbani Palit: Reverse nearest neighborhood based oversampling for multi-label datasets. Pattern Recognition Letters 2019.

Conference papers

• S Gupta, J Peliova, P Sadhukhan, P Kumar, P Vasilakopoulou, I Pappas; Conceptualizing Similarity Measurement in Data Marketplaces, UKAIS 2025

•  Payel Sadhukhan, Labani Halder, Sarbani Palit: A Hilbert-Curve based Encoding scheme for Privacy-preserving Nearest-Neighbor Classification. ICONIP 2024. 

• Payel Sadhukhan, Kausik Sengupta, Sarbani Palit, Tanujit Chakraborty. Knowing the class distinguishing abilities of the features, to build better decision-making models. AMCIS 2024.

• Payel Sadhukhan, Tanujit Chakraborty, Kausik Sengupta. Deploying model obfuscation: towards the privacy of decision-making models on shared platforms. AMCIS 2024.

• Sarbani Palit, Payel Sadhukhan. Parameter free undersampling for multi-label data. ICAART 2024. (Nominated for the Best Industrial Paper Award)

• Payel Sadhukhan, Sarbani Palit. Be informed of the known to catch the unknown. PRICAI 2023.

• Payel Sadhukhan, Arjun Pakrashi, Sarbani Palit, Brian Mac Namee: Integrating Unsupervised Clustering and Label-specific Oversampling to Tackle Imbalanced Multi-label Data, ICAART 2023. (Nominated for the Best Paper Award)

• Payel Sadhukhan, Arjun Pakrashi, Brian Mac Namee: Random Walk-steered Majority Undersampling, IEEE SMC 2022.

• Payel Sadhukhan: Exploring the Pertinence of Distance Functions for Nominal Multi-label Data, AIAI 2022.

• Payel Sadhukhan, C.A.Murthy: Multi-label Learning Through Minimum Spanning Tree- based Subset Selection and Feature Extraction. Canadian Conference on AI 2017.

• Payel Sadhukhan, Sarbani Palit: Fast Autonomous Crater Detection by Image Analysis-For Unmanned Landing on Unknown Terrain. ICISP 2016.

Get in touch at payel0410@gmail.com, p.sadhukhan.tmsl@ticollege.org