Handling Unbalanced Data Sets Using DBMUTE and NearMiss Methods to Improve Classification Performance of Yeast Data Sets

 (*)Bima Mahardika Wirawan Mail (Telkom University, Bandung, Indonesia)
 Mahendra Dwifebri Purbolaksono (Telkom University, Bandung, Indonesia)
 Fhira Nhita (Telkom University, Bandung, Indonesia)

(*) Corresponding Author

Submitted: June 9, 2023; Published: July 23, 2023


Yeast vacuole biogenesis was chosen as a model system for organelle assembly because most vacuole functions can be used for vegetative cell growth. Therefore it is possible to generate an extensive collection of mutants with defects in unbalanced vacuole assembly. With this in mind, we must find the structural balance of data in yeast. Imbalanced data is when there is an unbalanced distribution of data classes and the number of data classes is either more or lower than the number of other data classes. Our method uses the f1score performance matrix method and the balanced accuracy on DBMUTE and NearMiss undersampling. Previously, only a few studies explained the results of using a performance matrix and balanced accuracy. Then, find out the performance results of the f1 score and balanced accuracy and get the best score from the yeast datasets. In the study, a comparison between the imbalanced datasets using the undersampling method. Furthermore, to obtain the performance matrix results, use the f1 score and balance accuracy. After testing five yeast datasets, we performed an average f1 score and balance accuracy with the highest average NearMiss f1 score of 62.23% and the highest average balanced accuracy of 78.59%.


Imbalance Data; DBMUTE; NearMiss; Support Vector Machine; Undersampling

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