Under Consideration for Publication in Knowledge and Information Systems Improving Svm Classification on Imbalanced Time Series Data Sets with Ghost Points

By Suzan Köknar-Tezel, Longin Jan Latecki

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Indrė Žliobaitė
2. Faisal Kamiran
3. Sara Hajian
4. Lu Zhang
5. Toon Calders
6. Muhammad Bilal Zafar
7. Yongkai Wu
8. Asmita Kashid

Citations in the corpus

• Data preprocessing techniques for classification without discrimination (2011, methodology): “[17] present an innovative approach that augments the minority class by adding synthetic points in distance spaces then use Support Vector Machines for classification.”
• Quantifying explainable discrimination and removing illegal discrimination in automated decision making (2013, methodology): “Koknar-Tezel and Latecki [28] present an innovative approach that augments the minority class by adding synthetic points in distance spaces and then use Support Vector Machines for classification.”