Feature selection in high-dimensional data is an important part of the data mining process and is widely used in bioinformatics, statistics and image processing fields. Successfully selecting informative features can significantly improve learning accuracy and improve result comprehensibility. However, it is a challenging problem to select features accurately and efficiently from high-dimensional data. In this paper, we propose a Weighted Sparse Regression with Mutual Information (WSRMI) for selecting structural features. Differing from traditional sparse feature selection models that focus solely on either feature correlations or feature importance, the proposed model integrates both aspects through a mutual-information-based weighting mechanism. The proposed model can be effectively applied to regression and binary classification tasks, making it more general and practical for real-world applications. The proposed model is statistically compared with several existing classical models over randomly generated classification and benchmark datasets. Experimental results show that the proposed model is more effective at selecting the informative features with a superior prediction performance than the comparative ones.

With the rapid growth of data, feature selection has become essential for improving machine learning performance. However, most existing unsupervised feature selection methods rely on greedy strategies, which often lead to suboptimal solutions. Moreover, traditional information–theoretic approaches are primarily designed for discrete data and require discretization when applied to continuous data, potentially causing information loss. To address these issues, this paper proposes a global unsupervised feature selection method based on fuzzy mutual information (UFS-FMI). The proposed method integrates fuzzy set theory with information measures to quantify feature relevance and redundancy, and formulates a fractional optimization model. A combination of projection neural networks and kWTA neural networks is employed to achieve global optimization. Experimental results on nine UCI benchmark datasets demonstrate that UFS-FMI consistently outperforms several representative methods in terms of classification accuracy, clustering accuracy, and normalized mutual information (NMI). In particular, on datasets such as Movement_libras, Ionosphere, and Control, the proposed method achieves significantly improved classification performance, confirming its effectiveness and robustness.