Modeling using support vector machines on imbalanced data: A case study on the prediction of the sightings of Irrawaddy dolphins

2015 ◽  
Author(s):  
Liew Chin Ying ◽  
Jane Labadin ◽  
Wang Yin Chai ◽  
Andrew Alek Tuen ◽  
Cindy Peter
Keyword(s):  
Support Vector Machines ◽  
Imbalanced Data ◽  
Support Vector ◽  
Vector Machines

Support Vector Machines Classification on Class Imbalanced Data: A Case Study with Real Medical Data

2021 ◽  
Vol 12 (4) ◽  
pp. 727-754
Author(s):  
Krystallenia Drosou ◽  
Stelios Georgiou ◽  
Christos Koukouvinos ◽  
Stella Stylianou
Keyword(s):  
Support Vector Machines ◽  
Imbalanced Data ◽  
Medical Data ◽  
Support Vector ◽  
Vector Machines

A new classification strategy for human activity recognition using cost sensitive support vector machines for imbalanced data

Kybernetes ◽  
2014 ◽  
Vol 43 (8) ◽  
pp. 1150-1164 ◽  
Author(s):  
Bilal M’hamed Abidine ◽  
Belkacem Fergani ◽  
Mourad Oussalah ◽  
Lamya Fergani
Keyword(s):  
Support Vector Machines ◽  
Probabilistic Models ◽  
Conditional Random Fields ◽  
Performance Metrics ◽  
Imbalanced Data ◽  
Sampling Technique ◽  
Support Vector ◽  
Data Set ◽  
Content Type ◽  
Vector Machines

Purpose – The task of identifying activity classes from sensor information in smart home is very challenging because of the imbalanced nature of such data set where some activities occur more frequently than others. Typically probabilistic models such as Hidden Markov Model (HMM) and Conditional Random Fields (CRF) are known as commonly employed for such purpose. The paper aims to discuss these issues. Design/methodology/approach – In this work, the authors propose a robust strategy combining the Synthetic Minority Over-sampling Technique (SMOTE) with Cost Sensitive Support Vector Machines (CS-SVM) with an adaptive tuning of cost parameter in order to handle imbalanced data problem. Findings – The results have demonstrated the usefulness of the approach through comparison with state of art of approaches including HMM, CRF, the traditional C-Support vector machines (C-SVM) and the Cost-Sensitive-SVM (CS-SVM) for classifying the activities using binary and ubiquitous sensors. Originality/value – Performance metrics in the experiment/simulation include Accuracy, Precision/Recall and F measure.

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