scholarly journals Classification of Imaginary motor task from Electroencephalographic Signals: A Comparison of Feature Selection Methods and Classification Algorithms

2017 ◽  
Author(s):  
H. J. Vélez-Lora
Keyword(s):  
Feature Selection ◽  
Motor Task ◽  
Classification Algorithms ◽  
Selection Methods ◽  
Electroencephalographic Signals

scholarly journals BETTER ALTERNATIVES FOR STEPWISE DISCRIMINANT ANALYSIS

2015 ◽  
Vol 1 (311) ◽  
Author(s):  
Katarzyna Stąpor
Keyword(s):  
Feature Selection ◽  
Discriminant Analysis ◽  
Tabu Search ◽  
Stepwise Discriminant Analysis ◽  
Selection Methods ◽  
Discrimination Power ◽  
Statistical Software ◽  
Software Packages ◽  
Benchmark Datasets

Discriminant Analysis can best be defined as a technique which allows the classification of an individual into several dictinctive populations on the basis of a set of measurements. Stepwise discriminant analysis (SDA) is concerned with selecting the most important variables whilst retaining the highest discrimination power possible. The process of selecting a smaller number of variables is often necessary for a variety number of reasons. In the existing statistical software packages SDA is based on the classic feature selection methods. Many problems with such stepwise procedures have been identified. In this work the new method based on the metaheuristic strategy tabu search will be presented together with the experimental results conducted on the selected benchmark datasets. The results are promising.

The relevance of feature selection methods to the classification of obsessive-compulsive disorder based on volumetric measures

2017 ◽  
Vol 222 ◽  
pp. 49-56 ◽  
Author(s):  
Lucas R. Trambaiolli ◽  
Claudinei E. Biazoli ◽  
Joana B. Balardin ◽  
Marcelo Q. Hoexter ◽  
João R. Sato
Keyword(s):  
Feature Selection ◽  
Obsessive Compulsive Disorder ◽  
Selection Methods ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Volumetric Measures

A Systematic Study of Feature Selection Methods for Learning to Rank Algorithms

2018 ◽  
Vol 8 (3) ◽  
pp. 46-67 ◽  
Author(s):  
Mehrnoush Barani Shirzad ◽  
Mohammad Reza Keyvanpour
Keyword(s):  
Feature Selection ◽  
State Of The Art ◽  
Learning To Rank ◽  
Future Research ◽  
Selection Methods ◽  
Ranking Models ◽  
Ranking Systems ◽  
Efficiency And Effectiveness ◽  
Selection For

This article describes how feature selection for learning to rank algorithms has become an interesting issue. While noisy and irrelevant features influence performance, and result in an overfitting problem in ranking systems, reducing the number of features by illuminating irrelevant and noisy features is a solution. Several studies have applied feature selection for learning to rank, which promote efficiency and effectiveness of ranking models. As the number of features and consequently the number of irrelevant and noisy features is increasing, systematic a review of Feature selection for learning to rank methods is required. In this article, a framework to examine research on feature selection for learning to rank (FSLR) is proposed. Under this framework, the authors review the most state-of-the-art methods and suggest several criteria to analyze them. FSLR offers a structured classification of current algorithms for future research to: a) properly select strategies from existing algorithms using certain criteria or b) to find ways to develop existing methodologies.

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