A BPSO-SVM algorithm based on memory renewal and enhanced mutation mechanisms for feature selection

Wei, Jiaxuan; Zhang, Ruisheng; Yu, Zhixuan; Hu, Rongjing; Tang, Jianxin; Gui, Chun; Yuan, Yongna

doi:10.1016/j.asoc.2017.04.061

Cited by 83 publications

(23 citation statements)

References 40 publications

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“…SVM is a popular wrapper feature selection method employed by many researchers in different fields (Laref, Losson, Sava, & Siadat, ; Liu & Fan, ; Wei et al, ). An SVM is built and developed on the Vapnik–Chervonenkis (VC) theory and structural risk minimization (SRM) principle balances and thus maximizes the geometric margin while minimizing the generalization error with the aim of achieving the best generalization ability.…”

Section: Methodsmentioning

confidence: 99%

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

Bonah

Huang

Ren

et al. 2019

J Food Process Engineering

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Rapid detection of bacterial foodborne pathogens is crucial in reducing the incidence of diseases associated with food contaminated with pathogens and toxins. This article presents a classification model of support vector machine (SVM) together with a metaheuristic framework using genetic algorithm (GA), particle swarm optimization (PSO), and grid searching (GS) optimization algorithms for bacterial foodborne pathogen classification and differentiation. LDA and SVM showed classification accuracies of training (90.3% and 91%) and prediction (89.5% and 90.6%), respectively using the Matlab classification learner app. Optimization of the modeling parameters c and g for SVM were performed to increase efficiency and classification accuracy. Simulated results show classification accuracies of 100% (training set) and 98.95% (prediction set) for five selected bacterial pathogens acquired using electronic nose dataset and PSO‐SVM model. Electronic nose recognition system made up of 12 metal oxide semiconductor sensors produced different distinctive response signals for each bacterial and could differentiate Escherichia coli, Escherichia coli O157: H7, Listeria monocytogenes, Salmonella enteritidis, and Salmonella Typhimurium. PSO‐SVM algorithm can be efficiently used in bacterial discrimination at the species and strain level by electronic nose and has the good ability both in learning and in generalization. Practical Applications Existing microbial methods depend on traditional culture‐based methods which are time‐wise lengthy, require trained and qualified personnel, and are not suitable as point‐of‐use (POU) sensing devices. The application of electronic nose for the detection and classification of bacterial foodborne pathogens have shown great promise and proven to be effective with increasing sensitivity and selectivity as compared to traditional methods. The ability for E‐nose to discriminate between individual bacteria colonies at both species and strain level is of great public health importance since most bacteria have many strains. Virulence and pathogenicity are often associated with only a subset of these strains and it is essential for a method to be able to differentiate between pathogenic and nonpathogenic strains during a foodborne outbreak.

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Section: Methodsmentioning

confidence: 99%

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

Bonah

Huang

Ren

et al. 2019

J Food Process Engineering

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“…So, the problem becomes changing size of the subset efficiently. Some of the wrapper based FS methods are binary partcile swarm optimization using SVM [8], FS using ant colony optimization (ACO) [9], adaptive β binary sailfish optimizer (AβBSF) [10], FS using hybrid of grey wolf optimizer(GWO) and whale optimization algorithm (WOA) [11] etc. • Embedded: This method is based on the combination of filter and wrapper methods.…”

Section: Introductionmentioning

confidence: 99%

EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

et al. 2020

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Selecting the most relevant features from a high dimensional dataset is always a challenging task. In this regard, the feature selection (FS) method acts as a solution to this problem mainly in the domain of data mining and machine learning. It aims at improving the performance of a learning model greatly by choosing the relevant features and ignoring the redundant ones. Besides, this also helps to achieve efficient use of space and time by the learning model under consideration. Though over the years, many metaheuristic algorithms have been proposed by the researchers to solve FS problem, still this is considered as the open research problem due to its enormous challenges. Particularly, these algorithms, at times, suffer from poor convergence because of the improper tuning of exploration and exploitation phases. Here lies the importance of the hybrid meta-heuristics which help to improve the searching capability and convergence rate of the parent algorithms. To this end, the present work introduces a new hybrid meta-heuristic FS model by combining two meta-heuristics-Harmony Search (HS) algorithm and Artificial Electric Field Algorithm (AEFA), which we have named as Electrical Harmony based Hybrid Meta-heurtistic (EHHM). The proposed hybrid meta-heuristic converges faster than its predecessors, thereby ensuring its capability to search efficiently. Usability of EHHM is examined by applying it on 18 standard UCI datasets. Moreover, to prove its supremacy, we have compared it with 10 state-of-the-art FS methods. Link to code implementation of proposed method: khalid0007/Metaheuristic-Algorithms/FS_AEFAhHS.

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“…Tables 3-7 also highlight the detailed comparative results observed in the present experiment with some other standard optimization algorithms such as simulated annealing (SA), GA, memetic algorithm (MA), mutation enhanced binary particle swarm optimization (ME-BPSO) [63], whale optimization algorithm-crossover mutation (WOA-CM) [64] and LHCMA [33]. The achieved reduced-feature sets along with highest accuracy were marked in bold in the Table Analyzing the observed outcomes, it can be said that our SFHSA-based FS technique has surpassed the above mentioned techniques.…”

Section: Resultsmentioning

confidence: 91%

Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm

Saha¹,

Ghosh

et al. 2020

Applied Sciences

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Nowadays, researchers aim to enhance man-to-machine interactions by making advancements in several domains. Facial emotion recognition (FER) is one such domain in which researchers have made significant progresses. Features for FER can be extracted using several popular methods. However, there may be some redundant/irrelevant features in feature sets. In order to remove those redundant/irrelevant features that do not have any significant impact on classification process, we propose a feature selection (FS) technique called the supervised filter harmony search algorithm (SFHSA) based on cosine similarity and minimal-redundancy maximal-relevance (mRMR). Cosine similarity aims to remove similar features from feature vectors, whereas mRMR was used to determine the feasibility of the optimal feature subsets using Pearson’s correlation coefficient (PCC), which favors the features that have lower correlation values with other features—as well as higher correlation values with the facial expression classes. The algorithm was evaluated on two benchmark FER datasets, namely the Radboud faces database (RaFD) and the Japanese female facial expression (JAFFE). Five different state-of-the-art feature descriptors including uniform local binary pattern (uLBP), horizontal–vertical neighborhood local binary pattern (hvnLBP), Gabor filters, histogram of oriented gradients (HOG) and pyramidal HOG (PHOG) were considered for FS. Obtained results signify that our technique effectively optimized the feature vectors and made notable improvements in overall classification accuracy.

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A BPSO-SVM algorithm based on memory renewal and enhanced mutation mechanisms for feature selection

Cited by 83 publications

References 40 publications

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm

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