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.

Feature selection using binary particle swarm optimization and support vector machines for medical diagnosis

2012 ◽  
Vol 57 (5) ◽  
Author(s):  
Mohammad Reza Daliri
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Particle Swarm ◽  
Support Vector ◽  
Emission Computed Tomography ◽  
Binary Particle Swarm Optimization ◽  
Data Set ◽  
Swarm Optimization ◽  
Vector Machines

AbstractIn this article, we propose a feature selection strategy using a binary particle swarm optimization algorithm for the diagnosis of different medical diseases. The support vector machines were used for the fitness function of the binary particle swarm optimization. We evaluated our proposed method on four databases from the machine learning repository, including the single proton emission computed tomography heart database, the Wisconsin breast cancer data set, the Pima Indians diabetes database, and the Dermatology data set. The results indicate that, with selected less number of features, we obtained a higher accuracy in diagnosing heart, cancer, diabetes, and erythematosquamous diseases. The results were compared with the traditional feature selection methods, namely, the F-score and the information gain, and a superior accuracy was obtained with our method. Compared to the genetic algorithm for feature selection, the results of the proposed method show a higher accuracy in all of the data, except in one. In addition, in comparison with other methods that used the same data, our approach has a higher performance using less number of features.

scholarly journals Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3028 ◽  
Author(s):  
Zina Li ◽  
Lina Qiu ◽  
Ruixin Li ◽  
Zhipeng He ◽  
Jun Xiao ◽  
...  
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Emotion Recognition ◽  
Frequency Domain ◽  
Particle Swarm ◽  
Feature Selection Method ◽  
Selection Method ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Average Accuracy

Electroencephalogram (EEG) signals have been widely used in emotion recognition. However, the current EEG-based emotion recognition has low accuracy of emotion classification, and its real-time application is limited. In order to address these issues, in this paper, we proposed an improved feature selection algorithm to recognize subjects’ emotion states based on EEG signal, and combined this feature selection method to design an online emotion recognition brain-computer interface (BCI) system. Specifically, first, different dimensional features from the time-domain, frequency domain, and time-frequency domain were extracted. Then, a modified particle swarm optimization (PSO) method with multi-stage linearly-decreasing inertia weight (MLDW) was purposed for feature selection. The MLDW algorithm can be used to easily refine the process of decreasing the inertia weight. Finally, the emotion types were classified by the support vector machine classifier. We extracted different features from the EEG data in the DEAP data set collected by 32 subjects to perform two offline experiments. Our results showed that the average accuracy of four-class emotion recognition reached 76.67%. Compared with the latest benchmark, our proposed MLDW-PSO feature selection improves the accuracy of EEG-based emotion recognition. To further validate the efficiency of the MLDW-PSO feature selection method, we developed an online two-class emotion recognition system evoked by Chinese videos, which achieved good performance for 10 healthy subjects with an average accuracy of 89.5%. The effectiveness of our method was thus demonstrated.

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