A Semi-Supervised clustering based classification model for classifying imbalanced data streams in the presence of scarcely labelled data

2022 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Kiran Bhowmick ◽  
Meera Narvekar
Keyword(s):  
Data Streams ◽  
Imbalanced Data ◽  
Classification Model ◽  
Supervised Clustering

scholarly journals A Novel Model for Imbalanced Data Classification

2020 ◽  
Vol 34 (04) ◽  
pp. 6680-6687
Author(s):  
Jian Yin ◽  
Chunjing Gan ◽  
Kaiqi Zhao ◽  
Xuan Lin ◽  
Zhe Quan ◽  
...  
Keyword(s):  
Imbalanced Data ◽  
Data Classification ◽  
Classification Performance ◽  
Classification Model ◽  
Proposed Model ◽  
Imbalanced Data Classification ◽  
Public Datasets ◽  
Distribution Cost ◽  
Novel Model ◽  
Learning Data

Recently, imbalanced data classification has received much attention due to its wide applications. In the literature, existing researches have attempted to improve the classification performance by considering various factors such as the imbalanced distribution, cost-sensitive learning, data space improvement, and ensemble learning. Nevertheless, most of the existing methods focus on only part of these main aspects/factors. In this work, we propose a novel imbalanced data classification model that considers all these main aspects. To evaluate the performance of our proposed model, we have conducted experiments based on 14 public datasets. The results show that our model outperforms the state-of-the-art methods in terms of recall, G-mean, F-measure and AUC.

scholarly journals GEP-based classifier with drift detection for mining imbalanced data streams

2020 ◽  
Vol 176 ◽  
pp. 41-49
Author(s):  
Joanna Jedrzejowicz ◽  
Piotr Jedrzejowicz
Keyword(s):  
Data Streams ◽  
Imbalanced Data
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