A conditional opposition-based particle swarm optimization for feature selection

© 2021 The Authors. Published by Taylor & Francis. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1080/09540091.2021.2002266Because of the existence of irrelevant, redundant, and noisy attributes in large datasets, the accuracy of a classification model has degraded. Hence, feature selection is a necessary pre-processing stage to select the important features that may considerably increase the efficiency of underlying classification algorithms. As a popular metaheuristic algorithm, particle swarm optimization has successfully applied to various feature selection approaches. Nevertheless, particle swarm optimization tends to suffer from immature convergence and low convergence rate. Besides, the imbalance between exploration and exploitation is another key issue that can significantly affect the performance of particle swarm optimization. In this paper, a conditional opposition-based particle swarm optimization is proposed and used to develop a wrapper feature selection. Two schemes, namely opposition-based learning and conditional strategy are introduced to enhance the performance of the particle swarm optimization. Twenty-four benchmark datasets are used to validate the performance of the proposed approach. Furthermore, nine metaheuristics are chosen for performance verification. The findings show the supremacy of the proposed approach not only in obtaining high prediction accuracy but also in small feature sizes

Classification of Myoelectric Signal using Spectrogram Based Window Selection

This paper presents a study of the classification of myoelectric signal using spectrogram with different window sizes. The electromyography (EMG) signals of 40 hand movement types are collected from 10 subjects through NinaPro database. By employing spectrogram, the EMG signals are represented in time-frequency representation.  Ten features are extracted from spectrogram for performance evaluation. In this study, two classifiers namely support vector machine (SVM) and linear discriminate analysis (LDA) are used to evaluate the performance of spectrogram features in the classification of EMG signals. To determine the best window size in spectrogram, three different Hanning window sizes are examined. The experimental results indicate that by applying spectrogram with optimize window size and LDA, the highest mean classification accuracy of 91.29% is obtained

A Detail Study of Wavelet Families for EMG Pattern Recognition

Wavelet transform (WT) has recently drawn the attention of the researchers due to its potential in electromyography (EMG) recognition system. However, the optimal mother wavelet selection remains a challenge to the application of WT in EMG signal processing. This paper presents a detail study for different mother wavelet function in discrete wavelet transform (DWT) and continuous wavelet transform (CWT). Additionally, the performance of different mother wavelet in DWT and CWT at different decomposition level and scale are also investigated. The mean absolute value (MAV) and wavelength (WL) features are extracted from each CWT and reconstructed DWT wavelet coefficient. A popular machine learning method, support vector machine (SVM) is employed to classify the different types of hand movements. The results showed that the most suitable mother wavelet in CWT are Mexican hat and Symlet 6 at scale 16 and 32, respectively. On the other hand, Symlet 4 and Daubechies 4 at the second decomposition level are found to be the optimal wavelet in DWT. From the analysis, we deduced that Symlet 4 at the second decomposition level in DWT is the most suitable mother wavelet for accurate classification of EMG signals of different hand movements.

Trustworthy and efficient routing algorithm for IoT-FinTech applications using non-linear Lévy Brownian generalized normal distribution optimization

This is an accepted manuscript of an article published by IEEE in IEEE Internet of Things Journal on 02/09/2021, available online https://ieeexplore.ieee.org/document/9527318 The accepted version of the publication may differ from the final published version.The huge advancement in the field of communication has pushed the innovation pace towards a new concept in the context of Internet of Things (IoT) named IoT for Financial Technology applications (IoT-FinTech). The main intention is to leverage the businesses’ income and reducing cost by facilitating the benefits enabled by IoT-FinTech technology. To do so, some of the challenging problems that mainly related to routing protocols in such highly dynamic, unreliable (due to mobility) and widely distributed network need to be carefully addressed. This paper therefore focuses on developing a new trustworthy and efficient routing mechanism to be used in routing data traffic over IoT-FinTech mobile networks. A new Non-linear Lévy Brownian Generalized Normal Distribution Optimization (NLBGNDO) algorithm is proposed to solve the problem of finding an optimal path from source to destination sensor nodes to be used in forwarding FinTech’s related data. We also propose an objective function to be used in maintaining trustworthiness of the selected relay-node candidates by introducing a trust-based friendship mechanism to be measured and applied during each selection process. The formulated model also considering node’s residual energy, experienced response time, and inter-node distance (to figure out density/sparsity ratio of sensor nodes). Results demonstrate that our proposed mechanism could maintain very wise and efficient decisions over the selection period in comparison with other methods

Analysis Of Spinal Electromyography Signal When Lifting An Object

Lifting and swinging are daily activities that human do using the spine.Furthermore,spine provides support during standing and walking.Therefore,it is very important in everyday activities and it will be inconvenient when it is injured.Technology has provided ways to machine and human integration in helping or supporting people in their daily tasks.To make this integration successful, machines or robots need to understand the human muscle activity.To do so,electromyography (EMG) a bio signal record the electricity generated by muscle was implemented.However,the signal often influenced by the unwanted noise.In this paper,the MVC normalization method is applied to determine the spinal EMG signal on lumbar multifidus muscle when lifting an object.In order to analyze the identity of spinal EMG signal,two statistical analyses are done;1) ANOVA analysis and 2)Boxplot analysis.The signal will go through 8th order Gaussian function or Exponential Weight Moving Average Filter before being analysed.Results show that Exponential Weight Moving Average Filter gives more consistent value compared to 8th order Gaussian function which is 0.0428V RMSE based on linear fitting done from the maximum amplitude gather from the boxplot analysis done

General Learning Equilibrium Optimizer: A New Feature Selection Method for Biological Data Classification

Finding relevant information from biological data is a critical issue for the study of disease diagnosis, especially when an enormous number of biological features are involved. Intentionally, the feature selection can be an imperative preprocessing step before the classification stage. Equilibrium optimizer (EO) is a recently established metaheuristic algorithm inspired by the principle of dynamic source and sink models when measuring the equilibrium states. In this research, a new variant of EO called general learning equilibrium optimizer (GLEO) is proposed as a wrapper feature selection method. This approach adopts a general learning strategy to help the particles to evade the local areas and improve the capability of finding promising regions. The proposed GLEO aims to identify a subset of informative biological features among a large number of attributes. The performance of the GLEO algorithm is validated on 16 biological datasets, where nine of them represent high dimensionality with a smaller number of instances. The results obtained show the excellent performance of GLEO in terms of fitness value, accuracy, and feature size in comparison with other metaheuristic algorithms

Binary atom search optimisation approaches for feature selection

Atom Search Optimisation (ASO) is a recently proposed metaheuristic algorithm that has proved to work effectively on several benchmark tests. In this paper, we propose the binary variants of atom search optimisation (BASO) for wrapper feature selection. In the proposed scheme, eight transfer functions from S-shaped and V-shaped families are used to convert the continuous ASO into the binary version. The proposed BASO approaches are employed to select a subset of significant features for efficient classification. Twenty-two well-known benchmark datasets acquired from the UCI machine learning repository are used for performance validation. In the experiment, the BASO with an optimal transfer function that contributes to the best classification performance is presented. The particle swarm optimisation (PSO), binary differential evolution (BDE), binary bat algorithm (BBA), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA) are used to evaluate the efficacy and efficiency of proposed approaches in feature selection. Our experimental results reveal the superiority of proposed BASO not only in high prediction accuracy but also in the minimal number of selected features

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

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

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

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