http://romisatriawahono.net/lecture/rm/survey/machine%20learning/Chandrashekar%20-%20Feature%20Selection%20Methods%20-%202014.pdf

A survey on feature selection methods

Feature selection in machine learning: A new perspective

Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a user, which is considered an important factor in human-machine-interaction. Many methods for feature extraction have been studied and the selection of both appropriate features and electrode locations is usually based on neuro-scientific findings. Their suitability for emotion recognition, however, has been tested using a small amount of distinct feature sets and on different, usually small data sets. A major limitation is that no systematic comparison of features exists. Therefore, we review feature extraction methods for emotion recognition from EEG based on 33 studies. An experiment is conducted comparing these features using machine learning techniques for feature selection on a self recorded data set. Results are presented with respect to performance of different feature selection methods, usage of selected feature types, and selection of electrode locations. Features selected by multivariate methods slightly outperform univariate methods. Advanced feature extraction techniques are found to have advantages over commonly used spectral power bands. Results also suggest preference to locations over parietal and centro-parietal lobes.

Feature Extraction and Selection for Emotion Recognition from EEG

https://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/1772_bol14_INS_FS_microarray.pdf

A review of microarray datasets and applied feature selection methods

This paper is about variable selection with the random forests algorithm in presence of correlated predictors. In high-dimensional regression or classification frameworks, variable selection is a difficult task, that becomes even more challenging in the presence of highly correlated predictors. Firstly we provide a theoretical study of the permutation importance measure for an additive regression model. This allows us to describe how the correlation between predictors impacts the permutation importance. Our results motivate the use of the recursive feature elimination (RFE) algorithm for variable selection in this context. This algorithm recursively eliminates the variables using permutation importance measure as a ranking criterion. Next various simulation experiments illustrate the efficiency of the RFE algorithm for selecting a small number of variables together with a good prediction error. Finally, this selection algorithm is tested on the Landsat Satellite data from the UCI Machine Learning Repository.

Correlation and variable importance in random forests

A plenitude of feature selection (FS) methods is available in the literature, most of them rising as a need to analyze data of very high dimension, usually hundreds or thousands of variables. Such data sets are now available in various application areas like combinatorial chemistry, text mining, multivariate imaging, or bioinformatics. As a general accepted rule, these methods are grouped in filters, wrappers, and embedded methods. More recently, a new group of methods has been added in the general framework of FS: ensemble techniques. The focus in this survey is on filter feature selection methods for informative feature discovery in gene expression microarray (GEM) analysis, which is also known as differentially expressed genes (DEGs) discovery, gene prioritization, or biomarker discovery. We present them in a unified framework, using standardized notations in order to reveal their technical details and to highlight their common characteristics as well as their particularities.

A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis

There are more than 20,000 genomic studies comprising 500,000 samples freely available in the Gene Expression Omnibus (GEO) database [1]. However, accessing these data requires complex computational steps, including structuring and formatting the clinical vocabulary used to annotate the samples. These complex steps hinder the accessibility of genomic datasets through visualization and analysis software platforms, such as GenePattern and R/Bioconductor, therefore hampering the pace of research.

InSilico DB [2] is an online platform that provides a complete collaborative solution for structuring and formatting clinical annotations from GEO, making GenePattern and R datasets one click away for researchers.

InSilico DB has made available powerful and intuitive online curation tools to structure the metadata of GEO datasets. The database is automatically updated daily, through GEO import pipelines. Datasets can have multiple annotations given by different users, and one user can have multiple versions of an annotation to suit different experimental questions.

The InSilico DB platform supports datasets from Affymetrix human gene expression platforms, which account for 2,900 studies comprising 110,000 samples, making InSilico DB the largest public database of manually curated human gene expression samples. In addition to the web interface, InSilico DB offers programmatic access through an R/Bioconductor package [3].

Future releases of InSilico DB will include Illumina RNA-Seq platform data and Affymetrix mouse gene expression data.

/pdf/insilico-db-an-online-platform-to-collaboratively-structure-2jjwjd8opk.pdf

V. de Schaetzen

Papers

A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis

InSilico DB: an online platform to collaboratively structure and export publicly available datasets from the Gene Expression Omnibus database