Genetic fuzzy classifier for benchmark cancer diagnosis
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 1063-1067 |
| Journal / Publication | IECON Proceedings (Industrial Electronics Conference) |
| Volume | 3 |
| Publication status | Published - 1997 |
Conference
| Title | Proceedings of the 1997 23rd Annual International Conference on Industrial Electronics, Control, and Instrumentation, IECON. Part 2 (of 4) |
|---|---|
| City | New Orleans, LA, USA |
| Period | 9 - 14 November 1997 |
Link(s)
Abstract
An effective fuzzy classifier is proposed for solving a benchmark cancer diagnosis problem. This system comprises the use of optimized fuzzy membership functions through Genetic Algorithms, while the associated rules are generated from numerical data. In addition, a modified nearest-neighbour method is recommended to remedy the drawback of rules confinement. The end result convinces that this approach has the ability to handle classification problems with large data dimension.
Citation Format(s)
Genetic fuzzy classifier for benchmark cancer diagnosis. / Ke, J. Y.; Tang, K. S.; Man, K. F.
In: IECON Proceedings (Industrial Electronics Conference), Vol. 3, 1997, p. 1063-1067.
In: IECON Proceedings (Industrial Electronics Conference), Vol. 3, 1997, p. 1063-1067.
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
