scholarly journals Competitive Particle Swarm Optimization for Multi-Category Text Feature Selection

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 602 ◽  
Author(s):  
Jaesung Lee ◽  
Jaegyun Park ◽  
Hae-Cheon Kim ◽  
Dae-Won Kim
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Text Categorization ◽  
Relative Effectiveness ◽  
Search Process ◽  
Feature Subset ◽  
Evolutionary Search ◽  
Swarm Optimization ◽  
Text Feature ◽  
Conventional Methods

Multi-label feature selection is an important task for text categorization. This is because it enables learning algorithms to focus on essential features that foreshadow relevant categories, thereby improving the accuracy of text categorization. Recent studies have considered the hybridization of evolutionary feature wrappers and filters to enhance the evolutionary search process. However, the relative effectiveness of feature subset searches of evolutionary and feature filter operators has not been considered. This results in degenerated final feature subsets. In this paper, we propose a novel hybridization approach based on competition between the operators. This enables the proposed algorithm to apply each operator selectively and modify the feature subset according to its relative effectiveness, unlike conventional methods. The experimental results on 16 text datasets verify that the proposed method is superior to conventional methods.

scholarly journals Hybrid Binary Particle Swarm Optimization Differential Evolution-Based Feature Selection for EMG Signals Classification

Axioms ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 79 ◽  
Author(s):  
Jingwei Too ◽  
Abdul Rahim Abdullah ◽  
Norhashimah Mohd Saad
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Classification Performance ◽  
Binary Particle Swarm Optimization ◽  
Discrete Wavelet ◽  
Feature Subset ◽  
Functional Rehabilitation ◽  
Swarm Optimization

To date, the usage of electromyography (EMG) signals in myoelectric prosthetics allows patients to recover functional rehabilitation of their upper limbs. However, the increment in the number of EMG features has been shown to have a great impact on performance degradation. Therefore, feature selection is an essential step to enhance classification performance and reduce the complexity of the classifier. In this paper, a hybrid method, namely, binary particle swarm optimization differential evolution (BPSODE) was proposed to tackle feature selection problems in EMG signals classification. The performance of BPSODE was validated using the EMG signals of 10 healthy subjects acquired from a publicly accessible EMG database. First, discrete wavelet transform was applied to decompose the signals into wavelet coefficients. The features were then extracted from each coefficient and formed into the feature vector. Afterward, BPSODE was used to evaluate the most informative feature subset. To examine the effectiveness of the proposed method, four state-of-the-art feature selection methods were used for comparison. The parameters, including accuracy, feature selection ratio, precision, F-measure, and computation time were used for performance measurement. Our results showed that BPSODE was superior, in not only offering a high classification performance, but also in having the smallest feature size. From the empirical results, it can be inferred that BPSODE-based feature selection is useful for EMG signals classification.

Feature Selection Method Based on Parallel Binary Immune Quantum-Behaved Particle Swarm Optimization

2012 ◽  
Vol 546-547 ◽  
pp. 1538-1543 ◽  
Author(s):  
Chao Chen ◽  
Hao Dong Zhu
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Feature Selection Method ◽  
Selection Method ◽  
Data Sets ◽  
Feature Subset ◽  
Swarm Optimization ◽  
Memory Space ◽  
Selection Algorithms

In order to enhance the operating speed and reduce the occupied memory space and filter out irrelevant or lower degree of features, feature selection algorithms must be used. However, most of existing feature selection methods are serial and are inefficient timely to be applied to massive text data sets, so it is a hotspot how to improve efficiency of feature selection by means of parallel thinking. This paper presented a feature selection method based on Parallel Binary Immune Quantum-Behaved Particle Swarm Optimization (PBIQPSO). The presented method uses the Binary Immune Quantum-Behaved Particle Swarm Optimization to select feature subset, takes advantage of multiple computing nodes to enhance time efficiency, so can acquire quickly the feature subsets which are more representative. Experimental results show that the method is effective.

scholarly journals A Novel Feature Selection Method Based on Extreme Learning Machine and Fractional-Order Darwinian PSO

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yuan-Yuan Wang ◽  
Huan Zhang ◽  
Chen-Hui Qiu ◽  
Shun-Ren Xia
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Fractional Order ◽  
Fitness Function ◽  
Particle Swarm ◽  
Feature Selection Method ◽  
Feature Subset ◽  
Swarm Optimization ◽  
Learning Machine

The paper presents a novel approach for feature selection based on extreme learning machine (ELM) and Fractional-order Darwinian particle swarm optimization (FODPSO) for regression problems. The proposed method constructs a fitness function by calculating mean square error (MSE) acquired from ELM. And the optimal solution of the fitness function is searched by an improved particle swarm optimization, FODPSO. In order to evaluate the performance of the proposed method, comparative experiments with other relative methods are conducted in seven public datasets. The proposed method obtains six lowest MSE values among all the comparative methods. Experimental results demonstrate that the proposed method has the superiority of getting lower MSE with the same scale of feature subset or requiring smaller scale of feature subset for similar MSE.

scholarly journals Feature Selection Using Particle Swarm Optimization in Text Categorization

2015 ◽  
Vol 5 (4) ◽  
pp. 231-238 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Setareh Heidari
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Text Categorization ◽  
Particle Swarm ◽  
Feature Selection Method ◽  
Feature Space ◽  
Classification Systems ◽  
Main Step ◽  
Data Set ◽  
Swarm Optimization

Abstract Feature selection is the main step in classification systems, a procedure that selects a subset from original features. Feature selection is one of major challenges in text categorization. The high dimensionality of feature space increases the complexity of text categorization process, because it plays a key role in this process. This paper presents a novel feature selection method based on particle swarm optimization to improve the performance of text categorization. Particle swarm optimization inspired by social behavior of fish schooling or bird flocking. The complexity of the proposed method is very low due to application of a simple classifier. The performance of the proposed method is compared with performance of other methods on the Reuters-21578 data set. Experimental results display the superiority of the proposed method.

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