Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine.

TL;DR: An overview of feature selection techniques and instability of the feature selection algorithm is provided and some of the solutions which can handle the different source of instability are presented.

TL;DR: A new protein-protein interactions prediction method called LightGBM-PPI, which uses one-core network and the crossover network for the Wnt-related pathway to predict PPIs, which can provide new ideas for drug design and disease prevention.

TL;DR: Experimental results under seven UCI datasets and eight gene expression datasets illustrate that the proposed NMRS-based attribute reduction method using Lebesgue and entropy measures in incomplete neighborhood decision systems is effective to select most relevant attributes with higher classification accuracy, as compared with representative algorithms.

TL;DR: A systematic review as mentioned in this paper provides researchers interested in feature selection (FS) for processing microarray data with comprehensive information about the main research directions for gene expression classification conducted during the recent seven years.

TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.

TL;DR: In this article, the authors present a procedure for having two or more judges independently categorize a sample of units and determine the degree, significance, and significance of the units. But they do not discuss the extent to which these judgments are reproducible, i.e., reliable.

TL;DR: In comparative timings, the new algorithms are considerably faster than competing methods and can handle large problems and can also deal efficiently with sparse features.

TL;DR: In this article, a test of the hypothesis that the samples are from the same population may be made by ranking the observations from from 1 to Σn i (giving each observation in a group of ties the mean of the ranks tied for), finding the C sums of ranks, and computing a statistic H. Under the stated hypothesis, H is distributed approximately as χ2(C − 1), unless the samples were too small, in which case special approximations or exact tables are provided.