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A Percentile Transition Ranking Algorithm Applied to Binarization of Continuous Swarm Intelligence Metaheuristics Read chapter Read first chapter

2018 | OriginalPaper | Chapter

A Numerical Classification Technique Based on Fuzzy Soft Set Using Hamming Distance

Authors : Iwan Tri Riyadi Yanto, Rd Rohmat Saedudin, Saima Anwar Lashari, Haviluddin

Published in: Recent Advances on Soft Computing and Data Mining

Publisher: Springer International Publishing

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Abstract

In recent decades, fuzzy soft set techniques and approaches have received a great deal of attention from practitioners and soft computing researchers. This article attempts to introduce a classifier for numerical data using similarity measure fuzzy soft set (FSS) based on Hamming distance, named HDFSSC. Dataset have been taken from UCI Machine Learning Repository and MIAS (Mammographic Image Analysis Society). The proposed modeling consists of four phases: data acquisition, feature fuzzification, training phase and testing phase. Later, head to head comparison between state of the art fuzzy soft set classifiers is provided. Experiment results showed that the proposed classifier provides better accuracy when compared to the baseline fuzzy soft set classifiers.

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Metadata
Title
A Numerical Classification Technique Based on Fuzzy Soft Set Using Hamming Distance
Authors
Iwan Tri Riyadi Yanto
Rd Rohmat Saedudin
Saima Anwar Lashari
Haviluddin
Copyright Year
2018
Book
Recent Advances on Soft Computing and Data Mining

Print ISBN: 978-3-319-72549-9

Electronic ISBN: 978-3-319-72550-5

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-72550-5_25

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