imbalance provides a set of tools to work with imbalanced datasets:novel oversampling algorithms, filtering of instances and evaluation ofsynthetic instances.
You can install imbalance from Github with:
# install.packages("devtools")devtools::install_github("ncordon/imbalance")Runpdfos algorithm onnewthyroid1 imbalanced dataset and plot acomparison between attributes.
library("imbalance")data(newthyroid1)newSamples<- pdfos(newthyroid1,numInstances=80)# Join new samples with old imbalanced datasetnewDataset<- rbind(newthyroid1,newSamples)# Plot a visual comparison between both datasetsplotComparison(newthyroid1,newDataset,attrs= names(newthyroid1)[1:3],cols=2,classAttr="Class")
After filtering examples withneater:
filteredSamples<- neater(newthyroid1,newSamples,iterations=500)#> [1] "12 samples filtered by NEATER"filteredNewDataset<- rbind(newthyroid1,filteredSamples)plotComparison(newthyroid1,filteredNewDataset,attrs= names(newthyroid1)[1:3])
Execute methodADASYN using the wrapper provided by the package,comparing imbalance ratios of the dataset before and after oversampling:
imbalanceRatio(glass0)#> [1] 0.4861111newDataset<- oversample(glass0,method="ADASYN")imbalanceRatio(newDataset)#> [1] 0.9722222
