scholarly journals Recursive Ensemble Approach for Incremental Learning of Non-Stationary Imbalanced Data

2014 ◽  
Vol 98 (17) ◽  
pp. 41-45
Author(s):  
Pradnya A.Jain ◽  
Roshani Raut (Ade) ◽  
P. R. Deshmukh
Keyword(s):  
Incremental Learning ◽  
Imbalanced Data ◽  
Ensemble Approach

scholarly journals SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary

2018 ◽  
Vol 61 ◽  
pp. 863-905 ◽  
Author(s):  
Alberto Fernandez ◽  
Salvador Garcia ◽  
Francisco Herrera ◽  
Nitesh V. Chawla
Keyword(s):  
Big Data ◽  
Open Source ◽  
Supervised Learning ◽  
Incremental Learning ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Multilabel Classification ◽  
Current State ◽  
Software Packages ◽  
State Of Affairs

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to different type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several different domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also significantly contributed to new supervised learning paradigms, including multilabel classification, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of different software packages - from open source to commercial. In this paper, marking the fifteen year anniversary of SMOTE, we reflect on the SMOTE journey, discuss the current state of affairs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.

A new over-sampling ensemble approach for imbalanced data

2021 ◽  
Author(s):  
Peng Liu ◽  
Xiaoyuan Liu ◽  
Bo Liu ◽  
Xinyi Chen
Keyword(s):  
Imbalanced Data ◽  
Ensemble Approach

Incremental learning imbalanced data streams with concept drift: The dynamic updated ensemble algorithm

2020 ◽  
Vol 195 ◽  
pp. 105694
Author(s):  
Zeng Li ◽  
Wenchao Huang ◽  
Yan Xiong ◽  
Siqi Ren ◽  
Tuanfei Zhu
Keyword(s):  
Data Streams ◽  
Incremental Learning ◽  
Concept Drift ◽  
Imbalanced Data ◽  
Ensemble Algorithm

scholarly journals An Online Classification Method for Fault Diagnosis of Railway Turnouts

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4627
Author(s):  
Dongxiu Ou ◽  
Yuqing Ji ◽  
Lei Zhang ◽  
Hu Liu
Keyword(s):  
Fault Diagnosis ◽  
Incremental Learning ◽  
Imbalanced Data ◽  
Monitoring Data ◽  
Railway Transportation ◽  
Training Time ◽  
Fault Recognition ◽  
Online Classification ◽  
Diagnosis Method ◽  
Operation Status

Railway turnout system is a key infrastructure to railway safety and efficiency. However, it is prone to failure in the field. Therefore, many railway departments have adopted a monitoring system to monitor the operation status of turnouts. With monitoring data collected, many researchers have proposed different fault-diagnosis methods. However, many of the existing methods cannot realize real-time updating or deal with new fault types. This paper—based on imbalanced data—proposes a Bayes-based online turnout fault-diagnosis method, which realizes incremental learning and scalable fault recognition. First, the basic conceptions of the turnout system are introduced. Next, the feature extraction and processing of the imbalanced monitoring data are introduced. Then, an online diagnosis method based on Bayesian incremental learning and scalable fault recognition is proposed, followed by the experiment with filed data from Guangzhou Railway. The results show that the scalable fault-recognition method can reach an accuracy of 99.11%, and the training time of the Bayesian incremental learning model reduces 29.97% without decreasing the accuracy, which demonstrates the high accuracy, adaptability and efficiency of the proposed model, of great significance for labor-saving, timely maintenance and further, safety and efficiency of railway transportation.

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