BACKGROUND: Semisupervised learning has attracted significant interest in gene expression analysis due to its ability to improve classification performance under limited labeled data, a common challenge arising from high dimensionality and small sample sizes. Existing semisupervised methods often rely on manifold learning to propagate label information; however, weak label connections in early learning stages can reduce their effectiveness. RESULTS: To address these limitations, we propose a semisupervised approach for dominant gene selection and classification (SADGSC) that employs feature decomposition to identify essential features while suppressing noise and redundancy. The proposed method adopts hinge loss instead of square loss for label prediction and constructs a projection matrix based on confidence-weighted features, leading to improved classification accuracy and interpretability. Biological analysis of the top-ranked genes (e.g., PGR, KRT14, TOX3, and FGF10) reveals enrichment in hormone signaling epithelial-mesenchymal transition, apoptosis, and oxidative stress pathways, demonstrating the biological relevance of the selected genes. Experimental evaluations conducted on eleven datasets, primarily gene expression datasets, show that SADGSC achieves competitive performance compared with state-of-the-art methods. CONCLUSIONS: The proposed SADGSC framework effectively addresses the challenges of limited labeled data and high dimensionality in gene expression analysis, providing strong classification performance and biologically meaningful gene selection. These findings highlight the potential of semisupervised learning for dominant gene discovery and robust classification in high-dimensional biological datasets.

In this paper, we propose Minimax Probability Machine for Multi-label data classification and is termed as Multi-Label Minimax Probability Machine (MLMPM). Based on data mean and covariance information, MLMPM builds a classifier that minimizes an upper bound on the mis-classification probability of unseen future data. For capturing label correlation we have considered asymmetric co-occurrency matrix into the model. The proposed model has also been extended to non-linear settings using the Mercer Kernel trick. To accelerate the training procedure, iterative weighted least squares is used to train the underlying optimization model efficiently. Extensive experimental comparisons of our proposed method with related multi-label algorithms on synthetic as well as real world multi-label datasets, along with Amazon rainforest satellite images dataset, prove its efficacy.

Traditional hinge loss function-based large-scale support vector machine (SVM) algorithms tend to perform poorly in the presence of noise, especially when the model is trained incrementally. In this paper, we propose an efficient stochastic quasi-Newton method-based twin parametric SVM using the pinball loss function (termed as SQN-PTWSVM), which is efficient and more robust to the presence of noise when compared to conventional hinge loss SVM for large-scale data scenarios. To establish the theoretical convergence of the method, a modified version of SQN-PTWSVM, termed as SQN-SPTWSVM, has also been proposed. It overcomes the poor convergence issue faced by stochastic gradient twin SVM thus resulting in a faster and reliable model. In SQN-SPTWSVM, the hyperplanes obtained are stable enough to handle noise and resampling issues that occur frequently in stochastic learning scenarios, leading to better generalization ability of the classifier. The proposed method has been extended to nonlinear scenarios as well. Moreover, batch versions of the proposed algorithms have also been introduced which significantly reduce the training time and memory requirement of SQN-PTWSVM and SQN-SPTWSVM. The experimental results on several benchmark datasets and activity recognition applications have shown that the performance of our method is better than the existing classifiers in terms of speed and accuracy.

We propose Twin SVM, a binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems, each of which is smaller than in a conventional SVM. The Twin SVM formulation is in the spirit of proximal SVMs via generalized eigenvalues. On several benchmark data sets, Twin SVM is not only fast, but shows good generalization. Twin SVM is also useful for automatically discovering two-dimensional projections of the data.

In this paper, we propose a novel approach, called Regularized Least Squares Fuzzy Support Vector Regression, to handle time series forecasting. Two key problems in time series forecasting are noise and non-stationarity. Here, we assign a higher membership value to data samples that contain more relevant information. The approach requires only a single matrix inversion, and for the linear case, the matrix order depends only on the dimension in which the data samples lie, and is independent of the number of samples.

In a recent publication, Lazaro et al. addressed the problem of simultaneously approximating a function and its derivative using support vector machines. In this paper, we propose a new approach termed as regularized least squares twin support vector regression, for the simultaneous learning of a function and its derivatives. The regressor is obtained by solving one of two related support vector machine-type problems, each of which is of a smaller size than the one obtained in Lazaro's approach. The proposed algorithm is simple and fast, as no quadratic programming problem needs to be solved. Effectively, only the solution of a pair of linear systems of equations is needed.