Pairwise dependence-based unsupervised feature selection

TL;DR: In this article, a new feature selection technique called Advanced Particle Swarm Optimization (APSO) which elects the most informative and significant features for diagnosing COVID-19 patients is proposed.

TL;DR: In this paper , an unsupervised feature selection method is performed through the matrix factorization of the generated orthogonal set and a dual-correlation model is utilized in the objective function of UFGOR to simultaneously consider both the local correlation in a set of selected features and the global correlation among the samples of a data.

TL;DR: In this article, three novel frameworks based on the widespread regression methods Ridge, LASSO and Elastic Net are established to perform the task of feature selection, which benefit from the joint advantages of the dual-manifold learning and the hesitant fuzzy correlation matrix (HFCM) of the features and samples.

TL;DR: Wang et al. as mentioned in this paper proposed a robust principal component analysis (RPCA) model to mitigate the impact of outliers and conduct feature selection simultaneously, which adopted σ -norm as reconstruction error (RE), which plays an important role in robust reconstruction.

TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.

TL;DR: In this article, the maximal statistical dependency criterion based on mutual information (mRMR) was proposed to select good features according to the maximal dependency condition. But the problem of feature selection is not solved by directly implementing mRMR.

TL;DR: This work derives an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection, and presents a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers).