Computers and Operations Research
An improved neural classification network for the two-group problem
Computers and Operations Research
Comparative evaluation of genetic algorithm and backpropagation for training neural networks
Information Sciences—Informatics and Computer Science: An International Journal
Reliable classification using neural networks: a genetic algorithm and backpropagation comparison
Decision Support Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Computers and Operations Research
Computers and Industrial Engineering
Thalassaemia classification by neural networks and genetic programming
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A mixed integer optimisation model for data classification
Computers and Industrial Engineering
Neural and statistical classifiers-taxonomy and two case studies
IEEE Transactions on Neural Networks
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
Improving the controllability of tilt interaction for mobile map-based applications
International Journal of Human-Computer Studies
A hybrid intelligent system for medical data classification
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Artificial neural networks (ANN) have a wide ranging usage area in the data classification problems. Backpropagation algorithm is classical technique used in the training of the artificial neural networks. Since this algorithm has many disadvantages, the training of the neural networks has been implemented with the binary and real-coded genetic algorithms. These algorithms can be used for the solutions of the classification problems. The real-coded genetic algorithm has been compared with other training methods in the few works. It is known that the comparison of the approaches is as important as proposing a new classification approach. For this reason, in this study, a large-scale comparison of performances of the neural network training methods is examined on the data classification datasets. The experimental comparison contains different real classification data taken from the literature and a simulation study. A comparative analysis on the real data sets and simulation data shows that the real-coded genetic algorithm may offer efficient alternative to traditional training methods for the classification problem.