scholarly journals A New Co-Evolution Binary Particle Swarm Optimization with Multiple Inertia Weight Strategy for Feature Selection

Informatics ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 21 ◽  
Author(s):  
Jingwei Too ◽  
Abdul Rahim Abdullah ◽  
Norhashimah Mohd Saad
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Grey Wolf Optimizer ◽  
Binary Particle Swarm Optimization ◽  
Feature Selection Problem ◽  
Swarm Optimization ◽  
Inertia Weight

Feature selection is a task of choosing the best combination of potential features that best describes the target concept during a classification process. However, selecting such relevant features becomes a difficult matter when large number of features are involved. Therefore, this study aims to solve the feature selection problem using binary particle swarm optimization (BPSO). Nevertheless, BPSO has limitations of premature convergence and the setting of inertia weight. Hence, a new co-evolution binary particle swarm optimization with a multiple inertia weight strategy (CBPSO-MIWS) is proposed in this work. The proposed method is validated with ten benchmark datasets from UCI machine learning repository. To examine the effectiveness of proposed method, four recent and popular feature selection methods namely BPSO, genetic algorithm (GA), binary gravitational search algorithm (BGSA) and competitive binary grey wolf optimizer (CBGWO) are used in a performance comparison. Our results show that CBPSO-MIWS can achieve competitive performance in feature selection, which is appropriate for application in engineering, rehabilitation and clinical areas.

scholarly journals EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization

Computation ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 12 ◽  
Author(s):  
Jingwei Too ◽  
Abdul Abdullah ◽  
Norhashimah Mohd Saad ◽  
Weihown Tee
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Performance Comparison ◽  
Feature Reduction ◽  
Binary Particle Swarm Optimization ◽  
Discrete Wavelet ◽  
Feature Selection Problem ◽  
Swarm Optimization ◽  
Emg Signal

Due to the increment in hand motion types, electromyography (EMG) features are increasingly required for accurate EMG signals classification. However, increasing in the number of EMG features not only degrades classification performance, but also increases the complexity of the classifier. Feature selection is an effective process for eliminating redundant and irrelevant features. In this paper, we propose a new personal best (Pbest) guide binary particle swarm optimization (PBPSO) to solve the feature selection problem for EMG signal classification. First, the discrete wavelet transform (DWT) decomposes the signal into multiresolution coefficients. The features are then extracted from each coefficient to form the feature vector. After which pbest-guide binary particle swarm optimization (PBPSO) is used to evaluate the most informative features from the original feature set. In order to measure the effectiveness of PBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA), modified binary tree growth algorithm (MBTGA), and binary differential evolution (BDE) were used for performance comparison. Our experimental results show the superiority of PBPSO over other methods, especially in feature reduction; where it can reduce more than 90% of features while keeping a very high classification accuracy. Hence, PBPSO is more appropriate for application in clinical and rehabilitation applications.

scholarly journals SDPSO: Spark Distributed PSO-based approach for feature selection and cancer disease prognosis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Khawla Tadist ◽  
Fatiha Mrabti ◽  
Nikola S. Nikolov ◽  
Azeddine Zahi ◽  
Said Najah
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Cancer Prognosis ◽  
Computational Techniques ◽  
Binary Particle Swarm Optimization ◽  
Cancer Disease ◽  
Swarm Optimization

AbstractThe Dimensionality Curse is one of the most critical issues that are hindering faster evolution in several fields broadly, and in bioinformatics distinctively. To counter this curse, a conglomerate solution is needed. Among the renowned techniques that proved efficacy, the scaling-based dimensionality reduction techniques are the most prevalent. To insure improved performance and productivity, horizontal scaling functions are combined with Particle Swarm Optimization (PSO) based computational techniques. Optimization algorithms are an interesting substitute to traditional feature selection methods that are both efficient and relatively easier to scale. Particle Swarm Optimization (PSO) is an iterative search algorithm that has proved to achieve excellent results for feature selection problems. In this paper, a composite Spark Distributed approach to feature selection that combines an integrative feature selection algorithm using Binary Particle Swarm Optimization (BPSO) with Particle Swarm Optimization (PSO) algorithm for cancer prognosis is proposed; hence Spark Distributed Particle Swarm Optimization (SDPSO) approach. The effectiveness of the proposed approach is demonstrated using five benchmark genomic datasets as well as a comparative study with four state of the art methods. Compared with the four methods, the proposed approach yields the best in average of purity ranging from 0.78 to 0.97 and F-measure ranging from 0.75 to 0.96.

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