Efficiently Predicting Hot Spots in PPIs by Combining Random Forest and Synthetic Minority Over-Sampling Technique.Hot spot residues bring into play the vital function in bioinformatics to find new medications such as drug design.  This paper presents a classification method combining with random forest classification and oversampling strategy to improve the training performance.  Random forest classification is then invoked to generate a set of forest trees for this oversampled training set.  This proposed method is capable of randomly selecting features and constructing a robust random forest to avoid overfitting the training set. 
Random Forest
Efficiently Predicting Hot Spots in PPIs by Combining Random Forest and Synthetic Minority Over-Sampling Technique.Hot spot residues bring into play the vital function in bioinformatics to find new medications such as drug design.  This paper presents a classification method combining with random forest classification and oversampling strategy to improve the training performance.  Random forest classification is then invoked to generate a set of forest trees for this oversampled training set.  This proposed method is capable of randomly selecting features and constructing a robust random forest to avoid overfitting the training set. 
Random Forest