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A robust fuzzy twin support vector machine with kernel-target alignment for binary classification
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AI
DOI：10.1016/j.engappai.2025.112189.png)
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Abstract
En 中文
Many algorithms similar to the twin version of the support vector machine and their variants have shown better results in the binary classification of nonlinear data points. But in the presence of outlier and noise, these algorithms exhibit low generalization efficiency. To alleviate this challenge, recently proposed, kernel-target alignment based fuzzy least square twin bounded support vector machine (KTA-FLSTBSVM) used fuzzy membership values and is solved using least squares. Inspired by this strategy, for further improvement, we propose a novel approach called decision support kernel-target alignment based fuzzy least square twin bounded support vector machine (DS-KFIFTBSVM). DS-KFIFTBSVM considers the kernelized fuzzy membership values with the regularized twin support vector machine and solves for linear and nonlinear data points using a functional iterative approach. In DS-KFIFTBSVM, the solution is obtained by solving a linearly convergent iterative scheme rather than solving quadratic programming problems. The proposed DS-KFIFTBSVM offers better generalization efficiency, which has been evaluated using both linear and Gaussian kernels, mostly on artificially developed and publicly accessible datasets with diverse dimensionalities. In terms of various performance evaluation metrics, including specificity, precision, false positive rate, rate of misclassification error, F_score, and geometric mean in linear and non-linear cases, DS-KFIFTBSVM outperforms various baseline approaches. It shows the highest accuracy in various datasets, including 98.1884 % for musk dataset (linear kernel) and 100 % for the glass dataset (Gaussian kernel). Further statistical analysis confirms its classification efficiency.
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