Feature Selection in High Dimensional Data by a Filter-Based Genetic Algorithm

2017 ◽  
pp. 506-521 ◽  
Author(s):  
Claudio De Stefano ◽  
Francesco Fontanella ◽  
Alessandra Scotto di Freca
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
High Dimensional Data ◽  
High Dimensional

scholarly journals Feature Selection for High Dimensional Data Using Weighted K-Nearest Neighbors and Genetic Algorithm

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 139512-139528
Author(s):  
Shuangjie Li ◽  
Kaixiang Zhang ◽  
Qianru Chen ◽  
Shuqin Wang ◽  
Shaoqiang Zhang
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
High Dimensional Data ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
K Nearest Neighbors ◽  
Selection For

Feature Selection Method Based on the Adaptive Genetic Algorithm-Kernel Partial Least Squares for High Dimensional Data

2012 ◽  
Vol 468-471 ◽  
pp. 1762-1766 ◽  
Author(s):  
Dong Yan ◽  
Shao Wei Liu ◽  
Jian Tang
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
High Dimensional Data ◽  
Feature Selection Method ◽  
High Dimensional ◽  
Adaptive Genetic Algorithm ◽  
Kernel Partial Least Squares

Feature selection for modeling the high dimensional data, such as the near-infrared spectrum (NIR) is very important. A novel modeling approach combined the adaptive genetic algorithm-kernel partial least squares (AGA-KPLS) is proposed. The KPLS algorithm is used to construct nonlinear models with the popular kernel based modeling technology. The AGA is used to select the optimal feature sub-set from the original high dimensional data, which also used to select the kernel parameters of the KPLS algorithm simultaneously. The experimental results based on the vibration frequency spectrum show that the proposed approach has better prediction performance than the normal GA-PLS method.

scholarly journals Feature Selection using Genetic Algorithm for Clustering high Dimensional Data

2018 ◽  
Vol 7 (2.11) ◽  
pp. 27 ◽  
Author(s):  
Kahkashan Kouser ◽  
Amrita Priyam
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
Feature Space ◽  
High Dimensional ◽  
Feature Subset ◽  
Data Set ◽  
Optimal Feature Subset ◽  
Optimal Feature

One of the open problems of modern data mining is clustering high dimensional data. For this in the paper a new technique called GA-HDClustering is proposed, which works in two steps. First a GA-based feature selection algorithm is designed to determine the optimal feature subset; an optimal feature subset is consisting of important features of the entire data set next, a K-means algorithm is applied using the optimal feature subset to find the clusters. On the other hand, traditional K-means algorithm is applied on the full dimensional feature space.    Finally, the result of GA-HDClustering  is  compared  with  the  traditional  clustering  algorithm.  For comparison different validity  matrices  such  as  Sum  of  squared  error  (SSE),  Within  Group average distance (WGAD), Between group distance (BGD), Davies-Bouldin index(DBI),   are used .The GA-HDClustering uses genetic algorithm for searching an effective feature subspace in a large feature space. This large feature space is made of all dimensions of the data set. The experiment performed on the standard data set revealed that the GA-HDClustering is superior to traditional clustering algorithm. 

scholarly journals Fault Detection of Diesel Engine Air and after-Treatment Systems with High-Dimensional Data: A Novel Fault-Relevant Feature Selection Method

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 259
Author(s):  
Qilan Ran ◽  
Yedong Song ◽  
Wenli Du ◽  
Wei Du ◽  
Xin Peng
Keyword(s):  
Genetic Algorithm ◽  
Diesel Engine ◽  
Fault Detection ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Diesel Engines ◽  
Canonical Correlation ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Relevant Variables

In order to reduce pollutants of the emission from diesel vehicles, complex after-treatment technologies have been proposed, which make the fault detection of diesel engines become increasingly difficult. Thus, this paper proposes a canonical correlation analysis detection method based on fault-relevant variables selected by an elitist genetic algorithm to realize high-dimensional data-driven faults detection of diesel engines. The method proposed establishes a fault detection model by the actual operation data to overcome the limitations of the traditional methods, merely based on benchmark. Moreover, the canonical correlation analysis is used to extract the strong correlation between variables, which constructs the residual vector to realize the fault detection of the diesel engine air and after-treatment system. In particular, the elitist genetic algorithm is used to optimize the fault-relevant variables to reduce detection redundancy, eliminate additional noise interference, and improve the detection rate of the specific fault. The experiments are carried out by implementing the practical state data of a diesel engine, which show the feasibility and efficiency of the proposed approach.

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