Exploiting fractal dimension and a distributed evolutionary approach to classify data streams with concept drifts

Applied Soft Computing ◽

10.1016/j.asoc.2018.11.009 ◽

2019 ◽

Vol 75 ◽

pp. 284-297 ◽

Author(s):

Gianluigi Folino ◽

Massimo Guarascio ◽

Giuseppe Papuzzo

Keyword(s):

Fractal Dimension ◽

Data Streams ◽

Evolutionary Approach ◽

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Online Learning Model for Handling Different Concept Drifts Using Diverse Ensemble Classifiers on Evolving Data Streams

Cybernetics & Systems ◽

10.1080/01969722.2019.1645996 ◽

2019 ◽

Vol 50 (7) ◽

pp. 579-608

Author(s):

S. Ancy ◽

D. Paulraj

Keyword(s):

Online Learning ◽

Data Streams ◽

Learning Model ◽

Ensemble Classifiers ◽

Evolving Data ◽

Diverse Ensemble ◽

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Mining Concept Drifts from Data Streams Based on Multi-Classifiers

21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07) ◽

10.1109/ainaw.2007.250 ◽

2007 ◽

Author(s):

Yue Sun ◽

Guojun Mao ◽

Xu Liu ◽

Chunnian Liu

Keyword(s):

Data Streams ◽

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Classifying Data Streams with Skewed Class Distributions and Concept Drifts

IEEE Internet Computing ◽

10.1109/mic.2008.119 ◽

2008 ◽

Vol 12 (6) ◽

pp. 37-49 ◽

Author(s):

Jing Gao ◽

Bolin Ding ◽

Wei Fan ◽

Jiawei Han ◽

Philip S. Yu

Keyword(s):

Data Streams ◽

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A Survey of Challenges Facing Streaming Data

Transactions on Machine Learning and Artificial Intelligence ◽

10.14738/tmlai.84.8579 ◽

2020 ◽

Vol 8 (4) ◽

pp. 63-73

Author(s):

Sikha Bagui ◽

Katie Jin

Keyword(s):

Data Reduction ◽

Data Streams ◽

Data Stream ◽

Stream Processing ◽

Streaming Data ◽

Data Detection ◽

Data Stream Processing ◽

The Face ◽

This survey performs a thorough enumeration and analysis of existing methods for data stream processing. It is a survey of the challenges facing streaming data. The challenges addressed are preprocessing of streaming data, detection and dealing with concept drifts in streaming data, data reduction in the face of data streams, approximate queries and blocking operations in streaming data.

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Reinforcement Learning Based Decision Tree Induction Over Data Streams with Concept Drifts

2018 IEEE International Conference on Big Knowledge (ICBK) ◽

10.1109/icbk.2018.00051 ◽

2018 ◽

Author(s):

Christopher Blake ◽

Eirini Ntoutsi

Keyword(s):

Reinforcement Learning ◽

Decision Tree ◽

Data Streams ◽

Decision Tree Induction ◽

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Mining Concept Drifts from Data Streams Based on Multi-classifiers

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2008.00093 ◽

2008 ◽

Vol 34 (1) ◽

Author(s):

Yue SUN

Keyword(s):

Data Streams ◽

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Mining decision rules on data streams in the presence of concept drifts

Expert Systems with Applications ◽

10.1016/j.eswa.2007.11.034 ◽

2009 ◽

Vol 36 (2) ◽

pp. 1164-1178 ◽

Author(s):

Cheng-Jung Tsai ◽

Chien-I. Lee ◽

Wei-Pang Yang

Keyword(s):

Data Streams ◽

Decision Rules ◽

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EACD: evolutionary adaptation to concept drifts in data streams

Data Mining and Knowledge Discovery ◽

10.1007/s10618-019-00614-6 ◽

2019 ◽

Vol 33 (3) ◽

pp. 663-694 ◽

Author(s):

Hossein Ghomeshi ◽

Mohamed Medhat Gaber ◽

Yevgeniya Kovalchuk

Keyword(s):

Data Streams ◽

Evolutionary Adaptation ◽

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A Low-Granularity Classifier for Data Streams with Concept Drifts and Biased Class Distribution

IEEE Transactions on Knowledge and Data Engineering ◽

10.1109/tkde.2007.1057 ◽

2007 ◽

Vol 19 (9) ◽

pp. 1202-1213 ◽

Author(s):

Peng Wang ◽

Haixun Wang ◽

Xiaochen Wu ◽

Wei Wang ◽

Baile Shi

Keyword(s):

Data Streams ◽

Class Distribution ◽

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Proactive drift detection: Predicting concept drifts in data streams using probabilistic networks

2016 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2016.7727279 ◽

2016 ◽

Author(s):

Kylie Chen ◽

Yun Sing Koh ◽

Patricia Riddle

Keyword(s):

Data Streams ◽

Probabilistic Networks ◽

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