An Ensemble of Enhanced Fuzzy Min Max Neural Networks for Data Classification
Abstract: An ensemble of
Enhanced Fuzzy Min Max (EFMM) neural networks for data classification is proposed
in this paper. The certified belief in strength (CBS) method is used to
formulate the ensembleEFMM model, with the aim to improve the performance of
individual EFMM networks. The CBS method is used to measure trustworthiness of
each individual EFMM network based on its reputation and strength indicators.
Trust is built from strong elements associated with the EFMM network, allowing
the CBS method to improve the performance of the ensemble model. An auction
procedure based on the first-price sealed-bid scheme is adopted for determining
the winning EFMM network in undertaking classification tasks. The effectiveness
of the ensemble model is demonstrated using a number of benchmark data sets. Comparing
with the existing EFMM networks, the proposed ensemble model is able to improve
classification accuracy rates in the empirical study.
Keywords: Multi-agent
classifier system, fuzzy min-max neural network, trust measurement, classification
accuracy
Author: Mohammed Falah
Mohammed, Taha H. Rassem
Journal Code: jptkomputergg170060
