Boosting the performance of the fuzzy min-max neural network in pattern classification tasks
Data(s) |
01/01/2006
|
---|---|
Resumo |
In this paper, a boosted Fuzzy Min-Max Neural Network (FMM) is proposed. While FMM is a learning algorithm which is able to learn new classes and to refine existing classes incrementally, boosting is a general method for improving accuracy of any learning algorithm. In this work, AdaBoost is applied to improve the performance of FMM when its classification results deteriorate from a perfect score. Two benchmark databases are used to assess the applicability of boosted FMM, and the results are compared with those from other approaches. In addition, a medical diagnosis task is employed to assess the effectiveness of boosted FMM in a real application. All the experimental results consistently demonstrate that the performance of FMM can be considerably improved when boosting is deployed.<br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
Springer |
Relação |
http://dro.deakin.edu.au/eserv/DU:30050269/chen-boostingtheperformance-2006.pdf http://dx.doi.org/10.1007/3-540-31662-0_29 |
Tipo |
Journal Article |