Training software (TS) case study: feature selection system (FSS) for computer personnel

1989 ◽  
Vol 12 (1) ◽  
pp. 21-33
Author(s):  
A. Rushinek ◽  
S. Rushinek
Keyword(s):  
Feature Selection ◽  
Selection System

Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system

2020 ◽  
pp. 147592172097970
Author(s):  
Liangliang Cheng ◽  
Vahid Yaghoubi ◽  
Wim Van Paepegem ◽  
Mathias Kersemans
Keyword(s):  
Feature Selection ◽  
Mahalanobis Distance ◽  
Classification System ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Classification Performance ◽  
Decision Boundary ◽  
Particle Swarm Optimizer ◽  
Selection Phase

The Mahalanobis–Taguchi system is considered as a promising and powerful tool for handling binary classification cases. Though, the Mahalanobis–Taguchi system has several restrictions in screening useful features and determining the decision boundary in an optimal manner. In this article, an integrated Mahalanobis classification system is proposed which builds on the concept of Mahalanobis distance and its space. The integrated Mahalanobis classification system integrates the decision boundary searching process, based on particle swarm optimizer, directly into the feature selection phase for constructing the Mahalanobis distance space. This integration (a) avoids the need for user-dependent input parameters and (b) improves the classification performance. For the feature selection phase, both the use of binary particle swarm optimizer and binary gravitational search algorithm is investigated. To deal with possible overfitting problems in case of sparse data sets, k-fold cross-validation is considered. The integrated Mahalanobis classification system procedure is benchmarked with the classical Mahalanobis–Taguchi system as well as the recently proposed two-stage Mahalanobis classification system in terms of classification performance. Results are presented on both an experimental case study of complex-shaped metallic turbine blades with various damage types and a synthetic case study of cylindrical dogbone samples with creep and microstructural damage. The results indicate that the proposed integrated Mahalanobis classification system shows good and robust classification performance.

Evolutionary Machine Learning with Minions: A Case Study in Feature Selection

2021 ◽  
pp. 1-1
Author(s):  
Nick Zhang ◽  
Abhishek Gupta ◽  
Zefeng Chen ◽  
Yew-Soon Ong
Keyword(s):  
Machine Learning ◽  
Feature Selection

scholarly journals FEATURE FILTERING AND SELECTION FOR DRY MATTER ESTIMATION ON PERENNIAL RYEGRASS: A CASE STUDY OF VEGETATION INDICES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1827-1831 ◽  
Author(s):  
G. T. Alckmin ◽  
L. Kooistra ◽  
A. Lucieer ◽  
R. Rawnsley
Keyword(s):  
Feature Selection ◽  
Perennial Ryegrass ◽  
Dry Matter ◽  
Selection Process ◽  
Vegetation Indices ◽  
Robust Model ◽  
Trade Offs ◽  
Initial Dataset ◽  
High Degree

<p><strong>Abstract.</strong> Vegetation indices (VIs) have been extensively employed as a feature for dry matter (DM) estimation. During the past five decades more than a hundred vegetation indices have been proposed. Inevitably, the selection of the optimal index or subset of indices is not trivial nor obvious. This study, performed on a year-round observation of perennial ryegrass (n&amp;thinsp;=&amp;thinsp;900), indicates that for this response variable (i.e. kg.DM.ha<sup>&amp;minus;1</sup>), more than 80% of indices present a high degree of collinearity (correlation&amp;thinsp;&amp;gt;&amp;thinsp;|0.8|.) Additionally, the absence of an established workflow for feature selection and modelling is a handicap when trying to establish meaningful relations between spectral data and biophysical/biochemical features. Within this case study, an unsupervised and supervised filtering process is proposed to an initial dataset of 97 VIs. This research analyses the effects of the proposed filtering and feature selection process to the overall stability of final models. Consequently, this analysis provides a straightforward framework to filter and select VIs. This approach was able to provide a reduced feature set for a robust model and to quantify trade-offs between optimal models (i.e. lowest root mean square error &amp;ndash; RMSE&amp;thinsp;=&amp;thinsp;412.27&amp;thinsp;kg.DM.ha<sup>&amp;minus;1</sup>) and tolerable models (with a smaller number of features &amp;ndash; 4 VIs and within 10% of the lowest RMSE.)</p>

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