Particle Swarm Optimization: A Wrapper-Based Feature Selection Method for Ransomware Detection and Classification

2020 ◽  
pp. 181-196 ◽  
Author(s):  
Muhammad Shabbir Abbasi ◽  
Harith Al-Sahaf ◽  
Ian Welch
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Feature Selection Method ◽  
Selection Method ◽  
Swarm Optimization

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.

scholarly journals A Multiple Core Execution for Multiobjective Binary Particle Swarm Optimization Feature Selection Method with the Kernel P System Framework

2017 ◽  
Vol 2017 ◽  
pp. 1-14
Author(s):  
Naeimeh Elkhani ◽  
Ravie Chandren Muniyandi
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Membrane Computing ◽  
Particle Swarm ◽  
Feature Selection Method ◽  
Selection Method ◽  
Binary Particle Swarm Optimization ◽  
P System ◽  
Processing Unit ◽  
Swarm Optimization

Membrane computing is a theoretical model of computation inspired by the structure and functioning of cells. Membrane computing models naturally have parallel structure, and this fact is generally for all variants of membrane computing like kernel P system. Most of the simulations of membrane computing have been done in a serial way on a machine with a central processing unit (CPU). This has neglected the advantage of parallelism in membrane computing. This paper uses multiple cores processing tools in MATLAB as a parallel tool to implement proposed feature selection method based on kernel P system-multiobjective binary particle swarm optimization to identify marker genes for cancer classification. Through this implementation, the proposed feature selection model will involve all the features of a P system including communication rule, division rule, parallelism, and nondeterminism.

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