Search

Your search keyword '"Drift detector"' showing total 5 results
Start Over Descriptor "Drift detector" Topic data stream
5 results on '"Drift detector"'

1. A Systematic and Novel Ensemble Construction Method for Handling Data Stream Challenges

Author

Samant, Rucha Chetan, Patil, Suhas H., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Chen, Joy Iong-Zong, editor, Tavares, João Manuel R. S., editor, and Shi, Fuqian, editor

Published
2022
Full Text
View/download PDF

2. Concept drift detection with quadtree-based spatial mapping of streaming data.

Author

Amador Coelho, Rodrigo, Bambirra Torres, Luiz Carlos, and Leite de Castro, Cristiano

Subjects
*MACHINE learning, *DATA mapping, *ONLINE education, *DETECTORS
Abstract

Online learning is a complex task, especially when the data stream changes its distribution over time. It's challenging to monitor and detect these changes to maintain the performance of the learning algorithm. In this work, we present a novel detection method built from a different perspective of other preexisting detectors from literature. It analyzes the space occupied by the data, assuming that it would be immutable unless changes in this space occur among data of different classes. The data is mapped into a quadtree-based memory structure that provides knowledge about which class (label) is dominant in a given region of the feature space. Drifts are detected by checking whether data assigned to a given class occupy spaces considered relevant to the other class. The proposed method was evaluated on benchmark binary classification problems. The results show that our method can compete with well-known drift detectors from the literature on synthetic and real-world datasets. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

3. A First Attempt to Construct Effective Concept Drift Detector Ensembles

Author

Woźniak, Michał, Ksieniewicz, Paweł, Kasprzak, Andrzej, Puchała, Karol, Ryba, Przemysław, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, and Choraś, Ryszard S., editor

Published
2017
Full Text
View/download PDF

4. Ensembles of Heterogeneous Concept Drift Detectors - Experimental Study

Author

Woźniak, Michał, Ksieniewicz, Paweł, Cyganek, Bogusław, Walkowiak, Krzysztof, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Saeed, Khalid, editor, and Homenda, Władysław, editor

Published
2016
Full Text
View/download PDF

5. Ensembles of Heterogeneous Concept Drift Detectors - Experimental Study

Author

Michał Woźniak, Bogusław Cyganek, Krzysztof Walkowiak, Paweł Ksieniewicz, Wroclaw University of Science and Technology, AGH University of Science and Technology [Krakow, PL] (AGH UST), Khalid Saeed, Władysław Homenda, and TC 8

Subjects
Data stream, Decision support system, Concept drift, Computer science, Network security, media_common.quotation_subject, [SHS.INFO]Humanities and Social Sciences/Library and information sciences, 02 engineering and technology, computer.software_genre, 020204 information systems, Critical success factor, Business decision mapping, 0202 electrical engineering, electronic engineering, information engineering, Quality (business), [INFO]Computer Science [cs], media_common, business.industry, Drift detector, Pattern classification, Data analysis, 020201 artificial intelligence & image processing, Data mining, business, computer
Abstract

Part 7: Decisions; International audience; For the contemporary enterprises, possibility of appropriate business decision making on the basis of the knowledge hidden in stored data is the critical success factor. Therefore, the decision support software should take into consideration that data usually comes continuously in the form of so-called data stream, but most of the traditional data analysis methods are not ready to efficiently analyze fast growing amount of the stored records. Additionally, one should also consider phenomenon appearing in data stream called concept drift, which means that the parameters of an using model are changing, what could dramatically decrease the analytical model quality. This work is focusing on the classification task, which is very popular in many practical cases as fraud detection, network security, or medical diagnosis. We propose how to detect the changes in the data stream using combined concept drift detection model. The experimental evaluations confirm its pretty good quality, what encourage us to use it in practical applications.

Published
2016

Catalog

Books, media, physical & digital resources
See catalog results