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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Relative Density-Based Intuitionistic Fuzzy SVM for Class Imbalance Learning.

Cui Fu¹, Shuisheng Zhou¹, Dan Zhang¹

¹School of Mathematics and Statistics, Xi'dian University, Xi'an 710071, China.

Entropy (Basel, Switzerland)

|January 21, 2023

Summary

A new algorithm, relative density-based intuitionistic fuzzy support vector machine (RIFSVM), improves imbalanced learning by handling noise and outliers. This approach enhances classification performance on imbalanced datasets.

Keywords:

class imbalance learning fuzzy support vector machine (FSVM)intuitionistic fuzzy number (IFN)relative density

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Area of Science:

Machine Learning
Data Mining
Pattern Recognition

Background:

Support Vector Machines (SVM) combined with intuitionistic fuzzy sets struggle with inaccurate prior distribution estimation, especially for imbalanced and non-normally distributed datasets.
This limitation reduces classification model performance in imbalanced learning scenarios, particularly in the presence of noise and outliers.

Purpose of the Study:

To propose a novel Relative Density-based Intuitionistic Fuzzy Support Vector Machine (RIFSVM) algorithm.
To address the challenges of imbalanced learning, noise, and outliers in classification tasks.

Main Methods:

RIFSVM estimates relative density using k-nearest-neighbor distances to compute intuitionistic fuzzy numbers.
Majority class instance fuzzy values are adjusted by the imbalance ratio, while minority instances receive intuitionistic fuzzy membership degrees.
The algorithm leverages relative density for prior information capture and the intuitionistic fuzzy score function for noise and outlier recognition.

Main Results:

The RIFSVM algorithm effectively reduces the influence of class imbalance and suppresses the impact of noise and outliers.
Experimental results on synthetic and public imbalanced datasets demonstrate superior performance compared to existing imbalanced classification algorithms.
Performance improvements were measured using metrics such as G-Means, F-Measures, and Area Under the Curve (AUC).

Conclusions:

The proposed RIFSVM algorithm offers a robust solution for imbalanced learning problems with noise and outliers.
RIFSVM significantly enhances classification accuracy and reliability on challenging imbalanced datasets.
This novel approach provides a valuable contribution to the field of machine learning for imbalanced data analysis.