RT Journal Article
T1 Genetic optimised serial hierarchical fuzzy classifier for breast cancer diagnosis
A1 Zhang, Xiao
A1 Onieva, Enrique
A1 Perallos, Asier
A1 Osaba, Eneko
AB Accurate early-stage medical diagnosis of breast cancer can improve the survival rates and fuzzy rule-base system (FRBS) has been a promising classification system to detect breast cancer. However, the existing classification systems involves large number of input variables for training and produces a large number of fuzzy rules, which lead to high complexity and barely acceptable accuracy. In this paper, we present a genetic optimised serial hierarchical FRBS, which incorporates lateral tuning of membership functions and optimisation of the rule base. The serial hierarchical structure of FRBS allows selecting and ranking the input variables, which reduces the system complexity and distinguish the importance of attributes in datasets. We conduct an experimental study on Original Wisconsin Breast Cancer Database and Wisconsin Breast Cancer Diagnostic Database from UCI Machine Learning Repository, and show that the proposed system can classify breast cancer accurately and efficiently.
SN 1758-0366
YR 2020
FD 2020
LK https://hdl.handle.net/11556/3389
UL https://hdl.handle.net/11556/3389
LA eng
NO Zhang , X , Onieva , E , Perallos , A & Osaba , E 2020 , ' Genetic optimised serial hierarchical fuzzy classifier for breast cancer diagnosis ' , International Journal of Bio-Inspired Computation , vol. 15 , no. 3 , pp. 194-205 . https://doi.org/10.1504/IJBIC.2020.107490
NO Publisher Copyright: © 2020 Inderscience Enterprises Ltd.
NO This work was supported in part by the National Natural Science Foundation of China under Grant 61902437, the Fundamental Research Funds for the Central Universities, South-Central University for Nationalities under Grant CZT19010, and the Research Start-up Funds of South-Central University for Nationalities under Grant YZZ18006.
DS TECNALIA Publications
RD 2 sept 2024