Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Tegjyot Singh Sethi"'
Publikováno v:
International Journal of Data Science and Analytics. 7:179-199
Due to different expertise levels, personal preference, or fatigue from long working of the crowd workers, the data obtained through crowdsourcing are usually unreliable. One big challenge is to obtain true information from such noisy data. Sloppines
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5fedbf39513ba50c3baa1c74591c78c9
https://doi.org/10.1007/s41060-018-0139-5
https://doi.org/10.1007/s41060-018-0139-5
Autor:
Mehmed Kantardzic, Tegjyot Singh Sethi
Publikováno v:
Ubiquity. 2018:1-14
While machine learning has proven to be promising in several application domains, our understanding of its behavior and limitations is still in its nascent stages. One such domain is that of cybersecurity, where machine learning models are replacing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::66175a56fd33855eb98a7f56cd9d3d27
https://doi.org/10.1145/3158346
https://doi.org/10.1145/3158346
Publikováno v:
WIREs Data Mining and Knowledge Discovery. 10
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::391d3901bb0c060ea7f64ab9233b26ee
https://doi.org/10.1002/widm.1327
https://doi.org/10.1002/widm.1327
Publikováno v:
Journal of Intelligent Information Systems. 46:179-211
Mining data streams is the process of extracting information from non-stopping, rapidly flowing data records to provide knowledge that is reliable and timely. Streaming data algorithms need to be one pass and operate under strict limitations of memor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dc96a4ed78e71bf1dbd7c9d706e18ce4
https://doi.org/10.1007/s10844-015-0358-3
https://doi.org/10.1007/s10844-015-0358-3
Operating in a dynamic real world environment requires a forward thinking and adversarial aware design for classifiers, beyond fitting the model to the training data. In such scenarios, it is necessary to make classifiers - a) harder to evade, b) eas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::58f8a4f92e4cf71463a4471a8e674eea
Autor:
Tegjyot Singh Sethi, Mehmed Kantardzic
Publikováno v:
INNS Conference on Big Data
Validating online stream classifiers has traditionally assumed the availability of labeled samples, which can be monitored over time, to detect concept drift. However, labeling in streaming domains is expensive, time consuming and in certain applicat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e6eaf6beff1c6e8868c8f976d813575
Publikováno v:
IJCNN
Imbalanced data streams are found in many real world applications such as spam email detection, and internet traffic data. The classification of such data is challenging, since data stream usually changes, and the model should be updated to maintain
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2291af964892bb3ae9452c51f8d1803f
https://doi.org/10.1109/ijcnn.2017.7965964
https://doi.org/10.1109/ijcnn.2017.7965964
Autor:
Mehmed Kantardzic, Tegjyot Singh Sethi
New classifier-independent, dynamic, unsupervised approach for detecting concept drift.Reduced number of false alarms and increased relevance of drift detection.Results comparable to supervised approaches, which require fully labeled streams.Our appr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e9a49c5b32e7ec53e1e6b2215bd890bc
Publikováno v:
Intelligence and Security Informatics ISBN: 9783319574622
PAISI
PAISI
The increasing scale and sophistication of cyber-attacks has led to the adoption of machine learning based classification techniques, at the core of cybersecurity systems. These techniques promise scale and accuracy, which traditional rule/signature
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::56b075caaad6bae545f99e595d4f662c
https://doi.org/10.1007/978-3-319-57463-9_4
https://doi.org/10.1007/978-3-319-57463-9_4