A Novel Rotation Forest Modality Based on Hybrid NNs: RF (ScPSO-NN).

Graphical abstract Highlights • Rotation Forest (RF) algorithm is based on Decision Tree (DT) classifiers. • RF achieves to a similar performance with hybrid NNs. • In this paper, RF (hybrid NNs) is designed by combining two efficient phenomena. • For this purpose, we examine the use of hybrid NNs as the base classifiers of RF. • Test process is realized on binary-medical pattern classification. • RF (hybrid NNs) is compared with base, hybrid and ensemble classifiers. • As a result, RF (ScPSO-NN) comes to the forefront for classification task. Abstract Neural Network (NN), hybrid NN methods and Rotation Forest (RF) ensemble classifier are preferred in pattern analysis owing to their ability for finding efficient solutions on different problems. NN architecture usually includes backpropagation type algorithms in which error is exposed to fluctuations. Hybrid NN methods are generally designed to improve the classification performance of NN. Scout Particle Swarm Optimization (ScPSO) is one of these optimization algorithms including the effective parts of Particle Swarm Optimization (PSO) and Artificial Bee Colony Optimization (ABC). Moreover, RF algorithm usually indicates the same performance as in hybrid NN methods, although it is comprised of Decision Tree (DT) classifiers. At this point, our paper investigates whether RF using the hybrid NNs can outperform other ensemble classifiers in binary-medical pattern classification, or not. With this intention, PSO, ABC and ScPSO are placed in NN algorithms instead of back propagation, and hybrid methods (PSO-NN, ABC-NN and ScPSO-NN) are realized. As a result, RF (PSO-NN), RF (ABC-NN) and RF (ScPSO-NN) architectures are obtained. Classification Accuracy (CA), Area Under Curve (AUC), Sensitivity, Specificity, F-measure, Gmean and Precision metrics are used for a statistical performance comparison, and a test based on 2-fold cross validation method was realized on five medical datasets. [ABSTRACT FROM AUTHOR]