Name
Abstract | ||
|---|---|---|
Classification is a task of supervised learning whose aim is to identify to which of a set of categories a new input element belongs. Probabilistic neural network is a variant of artificial neural network, which is simple in structure, easy for training and often used in classification problems. In this paper, the authors propose an improved probabilistic neural network model that employs biogeography-based optimisation to enhance the accuracy of the classification. The proposed approach was tested on 11 standard benchmark medical datasets from the machine-learning repository. Results show that the classification accuracy of the proposed improved probabilistic neural network model outperforms that of the traditional probabilistic neural network model. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1504/IJDMMM.2017.10006639 | INTERNATIONAL JOURNAL OF DATA MINING MODELLING AND MANAGEMENT |
Keywords | Field | DocType |
biogeography-based optimisation, probabilistic neural networks, PNNs, classification problem | Data mining,Computer science,Supervised learning,Probabilistic neural network,Time delay neural network,Artificial intelligence,Data classification,Artificial neural network,Machine learning | Journal |
Volume | Issue | ISSN |
9 | 2 | 1759-1163 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammed Alweshah | 1 | 44 | 5.00 |
| Abdelaziz I. Hammouri | 2 | 83 | 6.51 |
| Sara Tedmori | 3 | 56 | 7.13 |