Name
Title | ||
|---|---|---|
(eta)-Hill climbing algorithm with probabilistic neural network for classification problems |
Abstract | ||
|---|---|---|
Classification is a crucial step in the data mining field. The probabilistic neural network (PNN) is an efficient method developed for classification problems. The success factor of using PNN for classification problems implies in finding the proper weight during classification process. The main goal of this paper is to improve the performance of PNN by finding the best weight for the PNN using the recent local search approach called beta-hill-climbing (beta-HC) optimizer. This algorithm is an extension version of the traditional hill climbing algorithm in that it uses a stochastic operator to avoid local optima. The proposed approach is evaluated against 11 benchmark datasets,and the experimental results showed that the proposed beta-HC with PNN approach performed better in terms of classification accuracy than the original PNN, HC-PNN and other six well-established approaches using the same experimented benchmarks. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s12652-019-01543-4 | JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING |
Keywords | DocType | Volume |
Probabilistic neural network (PNN),beta-Hill-climbing,Optimization,Classification | Journal | 11.0 |
Issue | ISSN | Citations |
8.0 | 1868-5137 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammed Alweshah | 1 | 44 | 5.00 |
| Aram Al-Daradkeh | 2 | 0 | 0.34 |
| Mohammed Azmi Al-Betar | 3 | 620 | 43.69 |
| Ammar Almomani | 4 | 116 | 8.68 |
| Saleh Oqeili | 5 | 0 | 0.34 |