Fast Rotation Kernel Density Estimation over Data Streams

Lei, Runze; Wang, Pinghui; Li, Rundong; Jia, Peng; Zhao, Junzhou; Guan, Xiaohong; Deng, Chao

doi:10.1145/3447548.3467356

Cited by 6 publications

(1 citation statement)

References 25 publications

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“…Research interest in the field of similarity analysis continues in many areas such as Clustering [ 21 , 22 , 23 , 24 ], Classification [ 25 , 26 , 27 , 28 ], and Recommendation systems [ 29 , 30 , 31 , 32 , 33 ]. In data stream analysis, Similarity has many recent research articles as well [ 34 , 35 , 36 , 37 , 38 , 39 ]. Generally, similarity analysis is concerned with finding similar data points by measuring the distance between them for the purpose of exploring specific patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

Similarity-Based Adaptive Window for Improving Classification of Epileptic Seizures with Imbalance EEG Data Stream

Fatlawi

Kiss

2022

Entropy

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Data stream mining techniques have recently received increasing research interest, especially in medical data classification. An unbalanced representation of the classification’s targets in these data is a common challenge because classification techniques are biased toward the major class. Many methods have attempted to address this problem but have been exaggeratedly biased toward the minor class. In this work, we propose a method for balancing the presence of the minor class within the current window of the data stream while preserving the data’s original majority as much as possible. The proposed method utilized similarity analysis for selecting specific instances from the previous window. This group of minor-class was then added to the current window’s instances. Implementing the proposed method using the Siena dataset showed promising results compared to the Skew ensemble method and some other research methods.

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