SMOTE: Synthetic Minority Over-sampling Technique Our method of over-sampling the minority class involves creating synthetic minority class examples Experiments are performed using C4 5, Ripper and a Naive Bayes classifier
SMOTE: Synthetic Minority Over-sampling Technique We propose an over-sampling approach in which the minority class is over-sampled by cre-ating “synthetic” examples rather than by over-sampling with replacement
SMOTE: Synthetic Minority Over-sampling Technique A new approach to construct the classifiers from imbalanced datasets is proposed by combining SMOTE (synthetic minority over-sampling technique) and Biased-SVM (biased support vector machine) approaches, which some experimental results confirms can achieve better classifier performance
SMOTE: Synthetic Minority Over-sampling Technique - 百度学术 MISMOTE: synthetic minority over-sampling technique for multiple instance learning with imbalanced data Within the study field of machine learning, multi-instance classification aims to build a mathematical model from a set of examples to classify objects des