Paper Info
Title
Generalized Normalized Euclidean Distance Based Fuzzy Soft Set Similarity For Data Classification
Abstract
Classification is one of the data mining processes used to predict predetermined target classes with data learning accurately. This study discusses data classification using a fuzzy soft set method to predict target classes accurately. This study aims to form a data classification algorithm using the fuzzy soft set method. In this study, the fuzzy soft set was calculated based on the normalized Hamming distance. Each parameter in this method is mapped to a power set from a subset of the fuzzy set using a fuzzy approximation function. In the classification step, a generalized normalized Euclidean distance is used to determine the similarity between two sets of fuzzy soft sets. The experiments used the University of California (UCI) Machine Learning dataset to assess the accuracy of the proposed data classification method. The dataset samples were divided into training (75% of samples) and test (25% of samples) sets. Experiments were performed in MATLAB R2010a software. The experiments showed that: (1) The fastest sequence is matching function, distance measure, similarity, normalized Euclidean distance, (2) the proposed approach can improve accuracy and recall by up to 10.3436% and 6.9723%, respectively, compared with baseline techniques. Hence, the fuzzy soft set method is appropriate for classifying data.
Year
DOI
Venue
2021
10.32604/csse.2021.015628
COMPUTER SYSTEMS SCIENCE AND ENGINEERING
Keywords
DocType
Volume
Soft set, fuzzy soft set, classification, normalized euclidean distance, similarity
Journal
38
Issue
ISSN
Citations 
1
0267-6192
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Rahmat Hidayat100.34
Iwan Tri Riyadi Yanto200.34
Azizul Azhar Ramli300.34
Mohd Farhan Md Fudzee400.34
Ansari Saleh Ahmar500.68