Enhancing Robustness and Efficiency of Least Square Twin SVM via Granular Computing
This addresses robustness and efficiency issues in LSTSVM for machine learning practitioners, though it appears incremental as an enhancement to an existing model.
The paper tackles LSTSVM's sensitivity to noise, instability, and computational inefficiency by proposing GBLSTSVM and LS-GBLSTSVM, which use granular balls and regularization to improve robustness and scalability; experiments on benchmark datasets show they consistently outperform baseline models.
In the domain of machine learning, least square twin support vector machine (LSTSVM) stands out as one of the state-of-the-art models. However, LSTSVM suffers from sensitivity to noise and outliers, overlooking the SRM principle and instability in resampling. Moreover, its computational complexity and reliance on matrix inversions hinder the efficient processing of large datasets. As a remedy to the aforementioned challenges, we propose the robust granular ball LSTSVM (GBLSTSVM). GBLSTSVM is trained using granular balls instead of original data points. The core of a granular ball is found at its center, where it encapsulates all the pertinent information of the data points within the ball of specified radius. To improve scalability and efficiency, we further introduce the large-scale GBLSTSVM (LS-GBLSTSVM), which incorporates the SRM principle through regularization terms. Experiments are performed on UCI, KEEL, and NDC benchmark datasets; both the proposed GBLSTSVM and LS-GBLSTSVM models consistently outperform the baseline models.