Information Sciences: an International Journal
An investigation of neural networks for linear time-series forecasting
Computers and Operations Research
RBF-based neurodynamic nearest neighbor classification in real pattern space
Pattern Recognition
Temporal gene expression classification with regularised neural network
International Journal of Bioinformatics Research and Applications
A dynamic architecture for artificial neural networks
Neurocomputing
On the Time Series K-Nearest Neighbor Classification of Abnormal Brain Activity
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
DSP-based hierarchical neural network modulation signal classification
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Automated text classification using a dynamic artificial neural network model
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A new matching strategy for content based image retrieval system
Applied Soft Computing
| Hi-index | 12.06 |
Classification is the process of assigning an object to one of a set of classes based on its attributes. Classification problems have been examined in fields as diverse as biology, medicine, business, image recognition, and forensics. Developing more accurate and widely applicable classification methods has significant implications in these and many other fields. This paper presents a dynamic artificial neural network (DAN2) as an alternate approach for solving classification problems. We show DAN2 to be an effective approach and compare its performance with linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbor algorithms, support vector machines, and traditional artificial neural networks using benchmark and real-world application data sets. These data sets vary in the number of classes (two vs. multiple) and the source of the data (synthetic vs. real-world). We found DAN2 to be a very effective classification method for two-class data sets with accuracy improvements as high as 37.2% when compared to the other methods. We also introduce a hierarchical DAN2 model for multiple class data sets that shows marked improvements (up to 89%) over all other methods, and offers better accuracy in all cases.