Table 2 The machine learning parameters used for each of the different algorithms in WEKA
From: Detecting false positive sequence homology: a machine learning approach
Algorithm | Parameters |
|---|---|
Neural Network | weka.classifiers.functions.MultilayerPerceptron -L 0.1 -M 0.05 -N 3000 -V 0 -S 0 -E 40 -H a |
Support Vector Machine (SVM) | weka.classifiers.functions.SMO -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V −1 -W 1 -K “weka.classifiers.functions.supportVector.PolyKernel -C |
Random Forest | weka.classifiers.trees.RandomForest -I 10 -K 0 -S 1 |
Naive Bayes | weka.classifiers.bayes.NaiveBayes |
Logistic Regression | weka.classifiers.functions.Logistic -R 1.0E-8 -M −1 |
Meta-Classifier w/o Logistic Regression | weka.classifiers.meta.Stacking -X 10 -M “weka.classifiers.functions.MultilayerPerceptron -L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a” -S 1 -B “weka.classifiers.trees.RandomForest -I 10 -K 0 -S 1” -B “weka.classifiers.bayes.NaiveBayes ” -B “weka.classifiers.functions.SMO -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V −1 -W 1 -K “weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0”” |
Meta-Classifier w/Logistic Regression | weka.classifiers.meta.Stacking -X 10 -M “weka.classifiers.functions.MultilayerPerceptron -L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a“ -S 1 -B ”weka.classifiers.functions.Logistic -R 1.0E-8 -M −1” -B “weka.classifiers.functions.MultilayerPerceptron -L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a” -B “weka.classifiers.trees.RandomForest -I 10 -K 0 -S 1” -B “weka.classifiers.bayes.NaiveBayes ” -B “weka.classifiers.functions.SMO -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V −1 -W 1 -K “weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0”” |