arXiv subjects

Liyun Zeng

Publications and source records attributed to Liyun Zeng.

3 recordsLinked to original sources

arXiv2023

Sparse Learning and Class Probability Estimation with Weighted Support Vector Machines

Classification and probability estimation are fundamental tasks with broad applications across modern machine learning and data science, spanning fields such as biology, medicine, engineering, and computer science. Recent development of weighted Support Vector Machines (wSVMs) has demonstrated considerable promise in robustly and accurately predicting class probabilities and performing classification across a variety of problems (Wang et al., 2008). However, the existing framework relies on an $\ell^2$-norm regularized binary wSVMs optimization formulation, which is designed for dense features and exhibits limited performance in the presence of sparse features with redundant noise. Effective sparse learning thus requires prescreening of important variables for each binary wSVM to ensure accurate estimation of pairwise conditional probabilities. In this paper, we propose a novel class of wSVMs frameworks that incorporate automatic variable selection with accurate probability estimation for sparse learning problems. We developed efficient algorithms for variable selection by solving either the $\ell^1$-norm or elastic net regularized wSVMs optimization problems. Class probability is then estimated either via the $\ell^2$-norm regularized wSVMs framework applied to the selected variables, or directly through elastic net regularized wSVMs. The two-step approach offers a strong advantage in simultaneous automatic variable selection and reliable probability estimators with competitive computational efficiency. The elastic net regularized wSVMs achieve superior performance in both variable selection and probability estimation, with the added benefit of variable grouping, at the cost of increases compensation time for high dimensional settings. The proposed wSVMs-based sparse learning methods are broadly applicable and can be naturally extended to $K$-class problems through ensemble learning.

stat.ME↗

arXiv2023

Robust Brain MRI Image Classification with SIBOW-SVM

The majority of primary Central Nervous System (CNS) tumors in the brain are among the most aggressive diseases affecting humans. Early detection of brain tumor types, whether benign or malignant, glial or non-glial, is critical for cancer prevention and treatment, ultimately improving human life expectancy. Magnetic Resonance Imaging (MRI) stands as the most effective technique to detect brain tumors by generating comprehensive brain images through scans. However, human examination can be error-prone and inefficient due to the complexity, size, and location variability of brain tumors. Recently, automated classification techniques using machine learning (ML) methods, such as Convolutional Neural Network (CNN), have demonstrated significantly higher accuracy than manual screening, while maintaining low computational costs. Nonetheless, deep learning-based image classification methods, including CNN, face challenges in estimating class probabilities without proper model calibration. In this paper, we propose a novel brain tumor image classification method, called SIBOW-SVM, which integrates the Bag-of-Features (BoF) model with SIFT feature extraction and weighted Support Vector Machines (wSVMs). This new approach effectively captures hidden image features, enabling the differentiation of various tumor types and accurate label predictions. Additionally, the SIBOW-SVM is able to estimate the probabilities of images belonging to each class, thereby providing high-confidence classification decisions. We have also developed scalable and parallelable algorithms to facilitate the practical implementation of SIBOW-SVM for massive images. As a benchmark, we apply the SIBOW-SVM to a public data set of brain tumor MRI images containing four classes: glioma, meningioma, pituitary, and normal. Our results show that the new method outperforms state-of-the-art methods, including CNN.

stat.ME↗

arXiv2022

Linear Algorithms for Robust and Scalable Nonparametric Multiclass Probability Estimation

Multiclass probability estimation is the problem of estimating conditional probabilities of a data point belonging to a class given its covariate information. It has broad applications in statistical analysis and data science. Recently a class of weighted Support Vector Machines (wSVMs) has been developed to estimate class probabilities through ensemble learning for $K$-class problems (Wu, Zhang and Liu, 2010; Wang, Zhang and Wu, 2019), where $K$ is the number of classes. The estimators are robust and achieve high accuracy for probability estimation, but their learning is implemented through pairwise coupling, which demands polynomial time in $K$. In this paper, we propose two new learning schemes, the baseline learning and the One-vs-All (OVA) learning, to further improve wSVMs in terms of computational efficiency and estimation accuracy. In particular, the baseline learning has optimal computational complexity in the sense that it is linear in $K$. Though not being most efficient in computation, the OVA offers the best estimation accuracy among all the procedures under comparison. The resulting estimators are distribution-free and shown to be consistent. We further conduct extensive numerical experiments to demonstrate finite sample performance.

stat.ME↗