Floating search methods in feature selection
Pattern Recognition Letters
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
New reflection generator for simulated annealing in mixed-integer/continuous global optimization
Journal of Optimization Theory and Applications
Evolving Multilayer Perceptrons
Neural Processing Letters
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Feature selection with neural networks
Pattern Recognition Letters
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Non-parametric classifier-independent feature selection
Pattern Recognition
Constructing of the risk classification model of cervical cancer by artificial neural network
Expert Systems with Applications: An International Journal
A hybrid approach for feature subset selection using neural networks and ant colony optimization
Expert Systems with Applications: An International Journal
A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms
Expert Systems with Applications: An International Journal
Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients
Expert Systems with Applications: An International Journal
A multilayer perceptron-based medical decision support system for heart disease diagnosis
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Location determination of mobile devices for an indoor WLAN application using a neural network
Knowledge and Information Systems
A dependency-based search strategy for feature selection
Expert Systems with Applications: An International Journal
Knowledge and Information Systems
Enhancing the classification accuracy by scatter-search-based ensemble approach
Applied Soft Computing
Designing simulated annealing and subtractive clustering based fuzzy classifier
Applied Soft Computing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An approach based on ANFIS input selection and modeling for supplier selection problem
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Robotics and Computer-Integrated Manufacturing
Hi-index | 12.06 |
The back-propagation network (BPN) can be used in various fields. Nevertheless, different problems may require different parameter settings for network architectures. Rule of thumb or ''trial and error'' methods are usually used to determine them. However, these methods may lead worse parameter settings for network architectures. On the other hand, although a dataset may contain many features, not all features are beneficial for classification in BPN. Therefore, a simulated-annealing-based approach, denoted as SA+BPN, is proposed to obtain the optimal parameter settings for network architectures of BPN, and to select the beneficial subset of features which result in a better classification. In order to evaluate the proposed SA+BPN approach, datasets in UCI Machine Learning Repository are used to evaluate the performance of the proposed approach. The experimental results show that the parameter settings for network architectures obtained by the proposed approach are better than those of other approaches. When the feature selection is taken into consideration, the classification accuracy rates of most datasets are increased. Therefore, the developed approach can be utilized to find out the optimal parameter settings for network architectures of BPN, and discover the useful features effectively.