Selecting samples for labeling in unbalanced streaming data environments. - Hanqing Hu; Mehmed M. Kantardzic; Tegjyot Singh Sethi (ICAT, 2013)

RLS-A reduced labeled samples approach for streaming imbalanced data with concept drift. - Elaheh Arabmakki; Mehmed M. Kantardzic; Tegjyot Singh Sethi (IRI, 2014)

An ensemble classification approach for handling spatio-temporal drifts in partially labeled data streams. - Tegjyot Singh Sethi; Mehmed M. Kantardzic; Elaheh Arabmakki; Hanqing Hu (IRI, 2014)

Don't Pay for Validation: Detecting Drifts from Unlabeled data Using Margin Density. - Tegjyot Singh Sethi; Mehmed M. Kantardzic (INNS Conference on Big Data, 2015)

Ensemble Classifier for Imbalanced Streaming Data Using Partial Labeling. - Elaheh Arabmakki; Mehmed M. Kantardzic; Tegjyot Singh Sethi (IRI, 2016)

Monitoring Classification Blindspots to Detect Drifts from Unlabeled Data. - Tegjyot Singh Sethi; Mehmed M. Kantardzic; Elaheh Arabmakki (IRI, 2016)

A grid density based framework for classifying streaming data in the presence of concept drift. - Tegjyot Singh Sethi; Mehmed M. Kantardzic; Hanqing Hu (J. Intell. Inf. Syst., 2016)

A partial labeling framework for multi-class imbalanced streaming data. - Elaheh Arabmakki; Mehmed M. Kantardzic; Tegjyot Singh Sethi (IJCNN, 2017)

'Security Theater': On the Vulnerability of Classifiers to Exploratory Attacks. - Tegjyot Singh Sethi; Mehmed M. Kantardzic; Joung Woo Ryu (PAISI, 2017)

On the reliable detection of concept drift from streaming unlabeled data. - Tegjyot Singh Sethi; Mehmed M. Kantardzic (Expert Syst. Appl., 2017)

Handling adversarial concept drift in streaming data. - Tegjyot Singh Sethi; Mehmed M. Kantardzic (Expert Syst. Appl., 2018)

Data driven exploratory attacks on black box classifiers in adversarial domains. - Tegjyot Singh Sethi; Mehmed M. Kantardzic (Neurocomputing, 2018)

When Good Machine Learning Leads to Bad Security: Big Data (Ubiquity symposium). - Tegjyot Singh Sethi; Mehmed M. Kantardzic (Ubiquity, 2018)

A dynamic-adversarial mining approach to the security of machine learning. - Tegjyot Singh Sethi; Mehmed M. Kantardzic; Lingyu Lyu; Jiashun Chen (WIREs Data Mining Knowl. Discov., 2018)

Sloppiness mitigation in crowdsourcing: detecting and correcting bias for crowd scoring tasks. - Lingyu Lyu; Mehmed M. Kantardzic; Tegjyot Singh Sethi (Int. J. Data Sci. Anal., 2019)

No Free Lunch Theorem for concept drift detection in streaming data classification: A review. - Hanqing Hu; Mehmed M. Kantardzic; Tegjyot Singh Sethi (WIREs Data Mining Knowl. Discov., 2020)