A Comparative Review of SMOTE and ADASYN in Imbalanced Data Classification

A Comparative Review of SMOTE and ADASYN in Imbalanced Data Classification

In this thesis, the performance of two over-sampling techniques, SMOTE and ADASYN, is compared. The comparison is done on three imbalanced data sets using three different classification models and evaluation metrics, while varying the way the data is pre-processed. The results show that both SMOTE a...

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Bibliographic Details
Main Authors: Brandt, Jakob, Lanzén, Emil
Format: Others
Language:English
Published: Uppsala universitet, Statistiska institutionen 2021
Subjects:
Machine learning
supervised learning
classification
class imbalance
over-sampling
SMOTE
ADASYN
Sensitivity
F-measure
Matthews correlation coefficient
Probability Theory and Statistics
Sannolikhetsteori och statistik
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-432162

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-432162

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