… is synthetic data sampling. More specifically, we propose a novel self-adaptive synthetic
over-sampling (SASYNO) approach to … over-sampling technique), 8 ADASYN (adaptive synthetic …

… ADASYN (adaptive synthetic sampling approach) has been used to increase the performance
of the machine learning algorithm and to transform the unbalanced data set into balanced …

… In this paper, we propose a Modified Adaptive Synthetic Sampling Technique (M-ADASYN) …
an innovative adaptive synthetic sampling algorithm M-ADASYN for imbalanced data …

… New datasets were generated using over-sampling techniques ADASYN-N, ADASYN-KNN,
and SMOTE-N then tested using the NBC classification. Implementation with the classifier …

… under-sampling techniques in the second step, to remove noise … synthetic samples to create
for each minority example x i . In other words, ADASYN generates many synthetic samples …

… posed by imbalanced datasets is critical for accurate classification in this domain. This
paper presents an innovative approach to enhancing the Adaptive Synthetic Sampling (ADASYN) …

… Synthetic Minority Oversampling Technique (SMOTE) is one of the oversampling techniques
existing and the Adaptive Synthetic (Adasyn) … (KNN) is incorporated in Adasyn. In this study, …

… observed that adaptive synthetic oversampling approach can best improve the imbalance ratio
as … after applying NCL and ADASYN resampling techniques to get improved performance. …

… , if one don’t want to compromise neither the accuracy of the classification task nor running
time, then among the proposed sampling algorithms considered in this study, the ADASYN …

… are generate the synthetic samples for the minority class … synthetic samples generated by
ADASYN and the IR account between (minority class + synthetic points generated by ADASYN…