scholarly journals A Cooperative Coevolutionary Approach to Discretization-Based Feature Selection for High-Dimensional Data

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 613
Author(s):  
Yu Zhou ◽  
Junhao Kang ◽  
Xiao Zhang
Keyword(s):  
Feature Selection ◽  
State Of The Art ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Selection Methods ◽  
Swarm Optimization ◽  
Cut Points ◽  
Selection For ◽  
The Individual ◽  
Ranking Mechanism

Recent discretization-based feature selection methods show great advantages by introducing the entropy-based cut-points for features to integrate discretization and feature selection into one stage for high-dimensional data. However, current methods usually consider the individual features independently, ignoring the interaction between features with cut-points and those without cut-points, which results in information loss. In this paper, we propose a cooperative coevolutionary algorithm based on the genetic algorithm (GA) and particle swarm optimization (PSO), which searches for the feature subsets with and without entropy-based cut-points simultaneously. For the features with cut-points, a ranking mechanism is used to control the probability of mutation and crossover in GA. In addition, a binary-coded PSO is applied to update the indices of the selected features without cut-points. Experimental results on 10 real datasets verify the effectiveness of our algorithm in classification accuracy compared with several state-of-the-art competitors.

scholarly journals Opportunities and Challenges of Feature Selection Methods for High Dimensional Data: A Review

2021 ◽  
Vol 26 (1) ◽  
pp. 67-77
Author(s):  
Siva Sankari Subbiah ◽  
Jayakumar Chinnappan
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Large Scale ◽  
High Dimensional Data ◽  
Research Work ◽  
Basic Feature ◽  
High Dimensional ◽  
Selection Methods ◽  
Fast Development ◽  
Improved Accuracy

Now a day, all the organizations collecting huge volume of data without knowing its usefulness. The fast development of Internet helps the organizations to capture data in many different formats through Internet of Things (IoT), social media and from other disparate sources. The dimension of the dataset increases day by day at an extraordinary rate resulting in large scale dataset with high dimensionality. The present paper reviews the opportunities and challenges of feature selection for processing the high dimensional data with reduced complexity and improved accuracy. In the modern big data world the feature selection has a significance in reducing the dimensionality and overfitting of the learning process. Many feature selection methods have been proposed by researchers for obtaining more relevant features especially from the big datasets that helps to provide accurate learning results without degradation in performance. This paper discusses the importance of feature selection, basic feature selection approaches, centralized and distributed big data processing using Hadoop and Spark, challenges of feature selection and provides the summary of the related research work done by various researchers. As a result, the big data analysis with the feature selection improves the accuracy of the learning.

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.

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

On the scalability of feature selection methods on high-dimensional data

2017 ◽  
Vol 56 (2) ◽  
pp. 395-442 ◽  
Author(s):  
V. Bolón-Canedo ◽  
D. Rego-Fernández ◽  
D. Peteiro-Barral ◽  
A. Alonso-Betanzos ◽  
B. Guijarro-Berdiñas ◽  
...  
Keyword(s):  
Feature Selection ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Selection Methods
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