Biomedical Text Categorization Based on Ensemble Pruning and Optimized Topic Modelling

Table 2

Classification algorithms used to build the model library.

Classifier Group	Classification Algorithms
Bayesian Classifiers	Bayesian logistic regression (with Norm-based hyper-parameter selection), Bayesian logistic regression (with Cross-validated hyper-parameter selection), Bayesian logistic regression (with Specific value based hyper-parameter selection), Naive Bayes, Naive Bayes Multinomial
Function based classifiers	FLDA, Kernel Logistic Regression (with Poly Kernel), Kernel Logistic Regression (with Normalized Poly Kernel), LibLINEAR (with L2-regularized logistic regression), LibLINEAR (with L2-regularized L2-loss support vector classification), LibLINEAR (with L1-regularized logistic regression), LibSVM (with radial basis function), LibSVM (with linear kernel), LibSVM (with polynomial kernel), LibSVM (with sigmoid kernel), Multi-layer perceptron, radial basis function networks, Logistic regression, Gaussian radial basis function networks
Instance based classifiers	KNN (with K: 1), KNN (with K:2), KNN (with K:3), KNN (with K: 4), KNN (with K:5), KNN (with K:6), KNN (with K:7), KNN (with K:8), KNN (with K:9), KNN (with K:10)
Rule based classifiers	FURIA (with Product T-norm), FURIA (with Minimum T-norm), RIPPER
Decision tree classifiers	BFTree (Unpruned), BFTree (Post-pruning), BFTree (Pre-pruning), Functional Tree, C4.5 (J48), NBTree, Random Forest, Random Tree

Table 2 is reproduced from ONAN et al. [19, 20] (under the Creative Commons Attribution License/public domain).