Deakin University
Browse

File(s) under permanent embargo

Adaptive rough radial basis function neural network with prototype outlier removal

Version 2 2024-06-06, 08:10
Version 1 2019-08-09, 08:27
journal contribution
posted on 2024-06-06, 08:10 authored by PY Goh, SC Tan, WP Cheah, Chee Peng LimChee Peng Lim
A new rough neural network (RNN)-based model is proposed in this paper. The radial basis function network with dynamic decay adjustment (RBFNDDA) is applied to learn information directly from a data set and group it in terms of prototypes. Then, a neighborhood rough set-based procedure is applied to detect prototype outliers. This hybrid model is named rough RBFNDDA1. However, the removal of all outliers may cause information loss because some outliers may represent rare yet useful information in a classification task. As such, the parameters of a prototype outlier, i.e., its radius and weight, are exploited to gauge whether the information encoded by the prototype is meaningful. This hybrid model is named rough RBFNDDA2. The results from a benchmark experimental study show that rough RBFNDDA2 can retain meaningful prototype outliers and, at the same time, significantly reduce the number of prototypes from the original RBFNDDA model while maintaining classification accuracy. A real-world application in a power generation plant is used to evaluate and demonstrate the effectiveness of the proposed model.

History

Journal

Information sciences

Volume

505

Pagination

127-143

Location

Amsterdam, The Netherlands

ISSN

0020-0255

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Publisher

Elsevier

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC