Classification of wine quality with imbalanced data

2016 ◽  
Author(s):  
Gongzhu Hu ◽  
Tan Xi ◽  
Faraz Mohammed ◽  
Huaikou Miao
Keyword(s):  
Imbalanced Data ◽  
Wine Quality

scholarly journals LICIC: Less Important Components for Imbalanced Multiclass Classification

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 317 ◽  
Author(s):  
Vincenzo Dentamaro ◽  
Donato Impedovo ◽  
Giuseppe Pirlo
Keyword(s):  
Gene Expression ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Multiclass Classification ◽  
Cancer Diagnostics ◽  
Mass Spectrometry Data ◽  
High Dimensional ◽  
Or Gene ◽  
High Dimensional Datasets

Multiclass classification in cancer diagnostics, using DNA or Gene Expression Signatures, but also classification of bacteria species fingerprints in MALDI-TOF mass spectrometry data, is challenging because of imbalanced data and the high number of dimensions with respect to the number of instances. In this study, a new oversampling technique called LICIC will be presented as a valuable instrument in countering both class imbalance, and the famous “curse of dimensionality” problem. The method enables preservation of non-linearities within the dataset, while creating new instances without adding noise. The method will be compared with other oversampling methods, such as Random Oversampling, SMOTE, Borderline-SMOTE, and ADASYN. F1 scores show the validity of this new technique when used with imbalanced, multiclass, and high-dimensional datasets.

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