| On the reliable detection of concept drift from streaming unlabeled data TS Sethi, M Kantardzic Expert Systems with Applications 82, 77-99, 2017 | 171 | 2017 |
| No Free Lunch Theorem for concept drift detection in streaming data classification: A review H Hu, M Kantardzic, TS Sethi Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10 (2 …, 2020 | 72 | 2020 |
| Handling adversarial concept drift in streaming data TS Sethi, M Kantardzic Expert Systems with Applications 97, 18-40, 2018 | 54 | 2018 |
| Data driven exploratory attacks on black box classifiers in adversarial domains TS Sethi, M Kantardzic Neurocomputing 289, 129-143, 2018 | 51 | 2018 |
| A grid density based framework for classifying streaming data in the presence of concept drift TS Sethi, M Kantardzic, H Hu Journal of Intelligent Information Systems 46, 179-211, 2016 | 42 | 2016 |
| Don’t pay for validation: Detecting drifts from unlabeled data using margin density TS Sethi, M Kantardzic Procedia Computer Science 53, 103-112, 2015 | 40 | 2015 |
| Monitoring classification blindspots to detect drifts from unlabeled data TS Sethi, M Kantardzic, E Arabmakki 2016 IEEE 17th International Conference on Information Reuse and Integration …, 2016 | 17 | 2016 |
| ‘Security theater’: on the vulnerability of classifiers to exploratory attacks TS Sethi, M Kantardzic, JW Ryu Intelligence and Security Informatics: 12th Pacific Asia Workshop, PAISI …, 2017 | 16 | 2017 |
| RLS-A reduced labeled samples approach for streaming imbalanced data with concept drift E Arabmakki, M Kantardzic, TS Sethi Proceedings of the 2014 IEEE 15th International Conference on Information …, 2014 | 12 | 2014 |
| CPN-a hybrid model for software cost estimation CHVMK Hari, TS Sethi, BSS Kaushal, A Sharma 2011 IEEE Recent Advances in Intelligent Computational Systems, 902-906, 2011 | 11 | 2011 |
| When good machine learning leads to bad security: Big data (ubiquity symposium) TS Sethi, M Kantardzic Ubiquity 2018 (May), 1-14, 2018 | 9 | 2018 |
| A dynamic‐adversarial mining approach to the security of machine learning TS Sethi, M Kantardzic, L Lyu, J Chen Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2018 | 9 | 2018 |
| Ensemble classifier for imbalanced streaming data using partial labeling E Arabmakki, M Kantardzic, TS Sethi 2016 IEEE 17th International Conference on Information Reuse and Integration …, 2016 | 9 | 2016 |
| An ensemble classification approach for handling spatio-temporal drifts in partially labeled data streams TS Sethi, M Kantardzic, E Arabmakki, H Hu Proceedings of the 2014 IEEE 15th International Conference on Information …, 2014 | 8 | 2014 |
| SEEPC: a toolbox for software effort estimation using soft computing techniques CVMK Hari, T Singh Sethi International Journal of Computer Applications 31 (4), 12-19, 2011 | 7 | 2011 |
| Cluster analysis & Pso for software cost estimation TS Sethi, CHVMK Hari, BSS Kaushal, A Sharma Information Technology and Mobile Communication: International Conference …, 2011 | 7 | 2011 |
| Selecting samples for labeling in unbalanced streaming data environments H Hu, MM Kantardzic, TS Sethi 2013 XXIV International Conference on Information, Communication and …, 2013 | 5 | 2013 |
| Sloppiness mitigation in crowdsourcing: detecting and correcting bias for crowd scoring tasks L Lyu, M Kantardzic, TS Sethi International Journal of Data Science and Analytics 7, 179-199, 2019 | 4 | 2019 |
| A partial labeling framework for multi-class imbalanced streaming data E Arabmakki, M Kantardzic, TS Sethi 2017 International Joint Conference on Neural Networks (IJCNN), 1018-1025, 2017 | 3 | 2017 |
| The GC3 Framework: Grid Density Based Clustering for Classification of Streaming Data with Concept Drift TS Sethi University of Louisville, 2013 | 3 | 2013 |
Mehmed KantardzicProfessor of Computer Science, University of LouisvilleBestätigte E-Mail-Adresse bei louisville.edu
