An imbalanced class on a dataset is common classification problem. The effect of using datasets can cause decrease in the performance classifier. Resampling one solutions to this This study used 100 from 3 websites: UCI Machine Learning, Kaggle, and OpenML. Each will go through processing stages: resampling process, significance testing process between evaluation values combination classifier p...

Abstract Class imbalance occurs when the class distribution is not equal. Namely, one under-represented (minority class), and other has significantly more samples in data (majority class). The problem prevalent many real world applications. Generally, minority of interest. synthetic over-sampling technique (SMOTE) method considered most prominent for handling unbalanced data. SMOTE generates ne...

A novel technique of automatically selecting the best pairs of features and sampling techniques to predict the stage of prostate cancer is proposed in this study. The problem of class imbalance, which is prominent in most medical data sets is also addressed here. Three feature subsets obtained by the use of principal components analysis (PCA), genetic algorithm (GA) and rough sets (RS) based ap...

in China 200 years before Christ, and it smote search for means of protection against this implacable foe was causing a certain amount of Rome in the second century A.D. Our first knowledge to be gained. Thus it was observed that survivors of an initial attack were protected against later infections, and that the appearance presented by the pustules was one of varying severity. accurate written...

Service level agreement (SLA) is an essential part of cloud systems to ensure maximum availability of services for customers. With a violation of SLA, the provider has to pay penalties. Thus, being able to predict SLA violations favors both the customers and the providers. In this paper, we explore two machine learning models: Naive Bayes and Random Forest Classifiers to predict SLA violations....

Computational models to predict the developmental toxicity of compounds are built on imbalanced datasets wherein the toxicants outnumber the non-toxicants. Consequently, the results are biased towards the majority class (toxicants). To overcome this problem and to obtain sensitive but also accurate classifiers, we followed an integrated approach wherein (i) Synthetic Minority Over Sampling (SMO...

In many real classification problems the data are imbalanced, i.e., the number of instances for some classes are much higher than that of the other classes. Solving a classification task using such an imbalanced data-set is difficult due to the bias of the training towards the majority classes. The aim of this contribution is to analyse the performance of CORBFN, a cooperative-competitive evolu...

In classification, when the distribution of the training data among classes is uneven, the learning algorithm is generally dominated by the feature of the majority classes. The features in the minority classes are normally difficult to be fully recognized. In this paper, a method is proposed to enhance the classification accuracy for the minority classes. The proposed method combines Synthetic ...