Improved Nonnegative Matrix Factorization Based Feature Selection for High Dimensional Data Analysis

2013 ◽  
Vol 347-350 ◽  
pp. 2344-2348
Author(s):  
Lin Cheng Jiang ◽  
Wen Tang Tan ◽  
Zhen Wen Wang ◽  
Feng Jing Yin ◽  
Bin Ge ◽  
...  
Keyword(s):  
Feature Selection ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
High Dimensional Data ◽  
Nonnegative Matrix ◽  
High Dimensional ◽  
Feature Subset ◽  
Feature Extraction Method ◽  
Optimal Feature Subset ◽  
Optimal Feature

Feature selection has become the focus of research areas of applications with high dimensional data. Nonnegative matrix factorization (NMF) is a good method for dimensionality reduction but it cant select the optimal feature subset for its a feature extraction method. In this paper, a two-step strategy method based on improved NMF is proposed.The first step is to get the basis of each catagory in the dataset by NMF. Added constrains can guarantee these basises are sparse and mostly distinguish from each other which can contribute to classfication. An auxiliary function is used to prove the algorithm convergent.The classic ReliefF algorithm is used to weight each feature by all the basis vectors and choose the optimal feature subset in the second step.The experimental results revealed that the proposed method can select a representive and relevant feature subset which is effective in improving the performance of the classifier.

scholarly journals A Clustering-Guided Integer Brain Storm Optimizer for Feature Selection in High-Dimensional Data

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jia Yun-Tao ◽  
Zhang Wan-Qiu ◽  
He Chun-Lin
Keyword(s):  
Feature Selection ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Second Phase ◽  
Feature Subset ◽  
Search Performance ◽  
Brain Storm Optimization ◽  
Real World Datasets ◽  
Optimal Feature Subset ◽  
Optimal Feature

For high-dimensional data with a large number of redundant features, existing feature selection algorithms still have the problem of “curse of dimensionality.” In view of this, the paper studies a new two-phase evolutionary feature selection algorithm, called clustering-guided integer brain storm optimization algorithm (IBSO-C). In the first phase, an importance-guided feature clustering method is proposed to group similar features, so that the search space in the second phase can be reduced obviously. The second phase applies oneself to finding optimal feature subset by using an improved integer brain storm optimization. Moreover, a new encoding strategy and a time-varying integer update method for individuals are proposed to improve the search performance of brain storm optimization in the second phase. Since the number of feature clusters is far smaller than the size of original features, IBSO-C can find an optimal feature subset fast. Compared with several existing algorithms on some real-world datasets, experimental results show that IBSO-C can find feature subset with high classification accuracy at less computation cost.

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 Efficient Feature Subset Selection Algorithm for High Dimensional Data

2016 ◽  
Vol 6 (4) ◽  
pp. 1880 ◽  
Author(s):  
Smita Chormunge ◽  
Sudarson Jena
Keyword(s):  
Feature Selection ◽  
Information Gain ◽  
High Dimensional Data ◽  
Feature Subset Selection ◽  
High Dimensional ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Computational Performance ◽  
Optimal Feature Subset

<p>Feature selection approach solves the dimensionality problem by removing irrelevant and redundant features. Existing Feature selection algorithms take more time to obtain feature subset for high dimensional data. This paper proposes a feature selection algorithm based on Information gain measures for high dimensional data termed as IFSA (Information gain based Feature Selection Algorithm) to produce optimal feature subset in efficient time and improve the computational performance of learning algorithms. IFSA algorithm works in two folds: First apply filter on dataset. Second produce the small feature subset by using information gain measure. Extensive experiments are carried out to compare proposed algorithm and other methods with respect to two different classifiers (Naive bayes and IBK) on microarray and text data sets. The results demonstrate that IFSA not only produces the most select feature subset in efficient time but also improves the classifier performance.</p>

Efficient Feature Subset Selection Algorithm for High Dimensional Data

2016 ◽  
Vol 6 (4) ◽  
pp. 1880 ◽  
Author(s):  
Smita Chormunge ◽  
Sudarson Jena
Keyword(s):  
Feature Selection ◽  
Information Gain ◽  
High Dimensional Data ◽  
Feature Subset Selection ◽  
High Dimensional ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Computational Performance ◽  
Optimal Feature Subset

<p>Feature selection approach solves the dimensionality problem by removing irrelevant and redundant features. Existing Feature selection algorithms take more time to obtain feature subset for high dimensional data. This paper proposes a feature selection algorithm based on Information gain measures for high dimensional data termed as IFSA (Information gain based Feature Selection Algorithm) to produce optimal feature subset in efficient time and improve the computational performance of learning algorithms. IFSA algorithm works in two folds: First apply filter on dataset. Second produce the small feature subset by using information gain measure. Extensive experiments are carried out to compare proposed algorithm and other methods with respect to two different classifiers (Naive bayes and IBK) on microarray and text data sets. The results demonstrate that IFSA not only produces the most select feature subset in efficient time but also improves the classifier performance.</p>

scholarly journals An Asymmetric Chaotic Competitive Swarm Optimization Algorithm for Feature Selection in High-Dimensional Data

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1782
Author(s):  
Supailin Pichai ◽  
Khamron Sunat ◽  
Sirapat Chiewchanwattana
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Chaotic Map ◽  
High Dimensional Data ◽  
Quality Criteria ◽  
High Dimensional ◽  
Feature Subset ◽  
Swarm Optimization ◽  
Candidate Solution ◽  
Dimensional Classification

This paper presents a method for feature selection in a high-dimensional classification context. The proposed method finds a candidate solution based on quality criteria using subset searching. In this study, the competitive swarm optimization (CSO) algorithm was implemented to solve feature selection problems in high-dimensional data. A new asymmetric chaotic function was proposed and used to generate the population and search for a CSO solution. Its histogram is right-skewed. The proposed method is named an asymmetric chaotic competitive swarm optimization algorithm (ACCSO). According to the asymmetrical property of the proposed chaotic map, ACCSO prefers zero than one. Therefore, the solution is very compact and can achieve high classification accuracy with a minimal feature subset for high-dimensional datasets. The proposed method was evaluated on 12 datasets, with dimensions ranging from 4 to 10,304. ACCSO was compared to the original CSO algorithm and other metaheuristic algorithms. Experimental results show that the proposed method can increase accuracy and it reduces the number of selected features. Compared to different optimization algorithms with other wrappers, the proposed method exhibits excellent performance.

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