Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance.
Processing these properties, HHPSO SVM feature selection algorithm is robust in tackling high-dimensional feature selection tasks.
Later, to improve the high-dimensional feature selection, Liu et al. [6] attempt to learn discriminative high-dimensional features using dynamic sparsity group.
Motivation: Penalized regression methods have been adopted widely for high-dimensional feature selection and prediction in many bioinformatic and biostatistical contexts.
MetaboAnalyst and XCMS Online both provide comprehensive statistical analysis tools which include univariate, multivariate, high-dimensional feature selection, clustering, and supervised classification analysis.
The model itself is inappropriate for disease diagnostics because the high-dimensional feature selection space generated from the kernel function may distort the information conveyed by the original data [ 12, 27].
Available high-dimensional feature-selection methods often fall into one of the following three categories: (i) filter methods, which simply rank all genes according to the inherent features of the microarray data, and their algorithm complexities are low.
Therefore, we developed a novel high-dimensional feature-selection algorithm called a Chi-square test-based integrated rank gene and direct classifier (χ-IRG-DC), which inherits the advantages of TSG while overcoming the disadvantages documented above in feature selection.
As an important technique for handling high-dimensional data, feature selection plays an important role in pattern recognition and machine learning.
In multi-dimensional datasets, feature selection methods mainly use filter based approach to obtain an optimal feature subspace and wrapper methods to search for an optimal feature subset within this space.
With such high-dimensional data, feature selection methods are essentially classification tools used to identify gene clusters that reveal biologically meaningful relationships [ 1].
