To overcome the two-class imbalanced classification problem existing in the diagnosis of
breast cancer, a hybrid of Random Over Sampling Example, K-means and Support vector …

To overcome the two‐class imbalanced problem existing in the diagnosis of breast cancer, a
hybrid of K‐means and Boosted C5. 0 (K‐Boosted C5. 0) is proposed which is based on …

In the most of standard learning algorithms it is presumed or at least expected that
distributions governing on different classes of at-hand dataset are balanced; it means that …

A widespread global health concern among women is the incidence of the second most
leading cause of fatality which is breast cancer. Predicting the occurrence of breast cancer …

Imbalanced data classification is challenging task for various datasets in the real world. One
of technique to enlarge the sample in minority class is oversampling to fix size as majority …

To overcome the two class imbalance problem among breast cancer diagnosis, a hybrid
method by combining principal component analysis (PCA) and boosted C5. 0 decision tree …

Ensemble technique and under-sampling technique are both effective tools used for
imbalanced dataset classification problems. In this paper, a novel ensemble method …

Under-sampling is a popular data preprocessing method in dealing with class imbalance
problems, with the purposes of balancing datasets to achieve a high classification rate and …

In imbalanced learning methods, resampling methods modify an imbalanced dataset to form
a balanced dataset. Balanced data sets perform better than imbalanced datasets for many …

Class imbalance is an important problem, encountered in machine learning applications,
where one class (named as, the minority class) has extremely small number of instances …