Fuzzy support vector machine algorithm based on local outliers factor

Qingbao ZHANG; Zhe JU; Wanli ZHANG

doi:10.3969/j.issn.2095-1248.2024.06.010

Journal of Shenyang Aerospace University >

2024 , Vol. 41 >Issue 6: 90 - 96

DOI: https://doi.org/10.3969/j.issn.2095-1248.2024.06.010

Fundamental Science and Engineering

Fuzzy support vector machine algorithm based on local outliers factor

Qingbao ZHANG ,
Zhe JU ,
Wanli ZHANG

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College of Science，Shenyang Aerospace University，Shenyang 110136，China

Received date: 2024-03-30

Online published: 2025-02-05

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Abstract

Fuzzy support vector machine is a classification algorithm that combines support vector machine and fuzzy theory.The existing fuzzy support vector machine algorithms can overcome the impact of noise data to some extent，but they have cost sensitivity，leading to inaccurate estimation of the prior distribution of data.A new fuzzy support vector machine algorithm was proposed.When designing the fuzzy membership function of samples，this algorithm better captures the distribution information of data by using the outlier factor constructed by the similarity of sampling points and their neighborhood density.The optimized model is validated using UCI datasets，which proves its good performance.

Key words： fuzzy support vector machine; neighborhood density; outlier factor; membership function; imbalanced dataset

Cite this article

Qingbao ZHANG , Zhe JU , Wanli ZHANG . Fuzzy support vector machine algorithm based on local outliers factor[J]. Journal of Shenyang Aerospace University, 2024 , 41(6) : 90 -96 . DOI: 10.3969/j.issn.2095-1248.2024.06.010

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