An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species

Table 7

Supervised algorithms and parameter values in the experiments.

Algorithm	Parameter values
RF-BD^1	Number of trees: 100; Random selected attributes per node: 3^2; Number of maps: 20
RF-BDCS	Number of trees: 100; Random selected attributes per node: 3; Number of maps: 20
ROS (100%) + RF-BD	RS^3 = 100%
ROS (130%) + RF-BD	RS = 130%
SVM-BD	Regulation parameter: 1.0, 0.5, and 0.0; Number of iterations: 100 (by default); StepSize: 1.0 (by default); miniBatchFraction: 1.0 (percent of the dataset evaluated in each iteration 100%)
ROS (100%) + SVM-BD	RS = 100%
ROS (130%) + SVM-BD	RS = 130%

BD: big data.
, where is the number of attributes of the dataset.
RS: resampling size.