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Tegjyot Singh Sethi
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2020 – today
- 2020
- [j8]Hanqing Hu, Mehmed M. Kantardzic, Tegjyot Singh Sethi:
No Free Lunch Theorem for concept drift detection in streaming data classification: A review. WIREs Data Mining Knowl. Discov. 10(2) (2020)
2010 – 2019
- 2019
- [j7]Lingyu Lyu
, Mehmed M. Kantardzic, Tegjyot Singh Sethi:
Sloppiness mitigation in crowdsourcing: detecting and correcting bias for crowd scoring tasks. Int. J. Data Sci. Anal. 7(3): 179-199 (2019) - 2018
- [j6]Tegjyot Singh Sethi, Mehmed M. Kantardzic
Handling adversarial concept drift in streaming data. Expert Syst. Appl. 97: 18-40 (2018) - [j5]Tegjyot Singh Sethi
Data driven exploratory attacks on black box classifiers in adversarial domains. Neurocomputing 289: 129-143 (2018) - [j4]Tegjyot Singh Sethi, Mehmed M. Kantardzic:
When Good Machine Learning Leads to Bad Security: Big Data (Ubiquity symposium). Ubiquity 2018(May): 1:1-1:14 (2018) - [j3]Tegjyot Singh Sethi
A dynamic-adversarial mining approach to the security of machine learning. WIREs Data Mining Knowl. Discov. 8(3) (2018) - [i5]Tegjyot Singh Sethi, Mehmed M. Kantardzic:
Handling Adversarial Concept Drift in Streaming Data. CoRR abs/1803.09160 (2018) - [i4]Tegjyot Singh Sethi, Mehmed M. Kantardzic, Lingyu Lyu, Jiashun Chen:
A Dynamic-Adversarial Mining Approach to the Security of Machine Learning. CoRR abs/1803.09162 (2018) - [i3]Tegjyot Singh Sethi, Mehmed M. Kantardzic, Joung Woo Ryu:
Security Theater: On the Vulnerability of Classifiers to Exploratory Attacks. CoRR abs/1803.09163 (2018) - 2017
- [j2]Tegjyot Singh Sethi, Mehmed M. Kantardzic
On the reliable detection of concept drift from streaming unlabeled data. Expert Syst. Appl. 82: 77-99 (2017) - [c8]Elaheh Arabmakki, Mehmed M. Kantardzic
A partial labeling framework for multi-class imbalanced streaming data. IJCNN 2017: 1018-1025 - [c7]Tegjyot Singh Sethi, Mehmed M. Kantardzic
'Security Theater': On the Vulnerability of Classifiers to Exploratory Attacks. PAISI 2017: 49-63 - [i2]Tegjyot Singh Sethi, Mehmed M. Kantardzic:
Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains. CoRR abs/1703.07909 (2017) - [i1]Tegjyot Singh Sethi, Mehmed M. Kantardzic:
On the Reliable Detection of Concept Drift from Streaming Unlabeled Data. CoRR abs/1704.00023 (2017) - 2016
- [j1]Tegjyot Singh Sethi, Mehmed M. Kantardzic
A grid density based framework for classifying streaming data in the presence of concept drift. J. Intell. Inf. Syst. 46(1): 179-211 (2016) - [c6]Tegjyot Singh Sethi, Mehmed M. Kantardzic
Monitoring Classification Blindspots to Detect Drifts from Unlabeled Data. IRI 2016: 142-151 - [c5]Elaheh Arabmakki, Mehmed M. Kantardzic
Ensemble Classifier for Imbalanced Streaming Data Using Partial Labeling. IRI 2016: 257-260 - 2015
- [c4]Tegjyot Singh Sethi, Mehmed M. Kantardzic
Don't Pay for Validation: Detecting Drifts from Unlabeled data Using Margin Density. INNS Conference on Big Data 2015: 103-112 - 2014
- [c3]Tegjyot Singh Sethi, Mehmed M. Kantardzic
An ensemble classification approach for handling spatio-temporal drifts in partially labeled data streams. IRI 2014: 725-732 - [c2]Elaheh Arabmakki, Mehmed M. Kantardzic
RLS-A reduced labeled samples approach for streaming imbalanced data with concept drift. IRI 2014: 779-786 - 2013
- [c1]Hanqing Hu, Mehmed M. Kantardzic
Selecting samples for labeling in unbalanced streaming data environments. ICAT 2013: 1-7
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