Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy

Chen, Huiling; Yang, Bo; Wang, Su jing; Wang, Gang; Liu, Da You; Li, Huai zhong; Liu, Wen Bin

doi:10.1016/j.amc.2014.04.039

Cited by 91 publications

(47 citation statements)

References 23 publications

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“…To further improve and balance the relationship between the local exploitation and global exploitation, we use the time-varying acceleration coefficients (TVAC) [60, 61] and time-varying inertial weight (TVIW) [60, 61, 62]; the effectiveness of using TVAC and TVIW techniques on the acceleration coefficients and inertial weight have been verified. These two approaches dynamically update the acceleration coefficients and inertial weight during the iterations and can help the original PSO algorithm perform better in determining the region of the global solution and avoiding the case of the algorithm search procedure becoming trapped in local minima [60, 61, 62].…”

Section: Methodsmentioning

confidence: 99%

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Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

2017

PLoS ONE

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In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks.

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Section: Methodsmentioning

confidence: 99%

“…These two approaches dynamically update the acceleration coefficients and inertial weight during the iterations and can help the original PSO algorithm perform better in determining the region of the global solution and avoiding the case of the algorithm search procedure becoming trapped in local minima [60, 61, 62]. …”

Section: Methodsmentioning

confidence: 99%

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

2017

PLoS ONE

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“…Chen et al [92] proposed a parallel time variant particle swarm optimization Zhang and Zhang [98] employed the social emotional optimization algorithm (SEOA) for machine training and parameter settings for SVM. They modeled machine training for SVM as a multi-parameter optimization problem which is solved by SEOA.…”

Section: Parameter Optimizationmentioning

confidence: 99%

Review of data mining applications for quality assessment in manufacturing industry: support vector machines

Rostami

Dantan

Homri

2015

Int. J. Metrol. Qual. Eng.

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. AbstractIn many modern manufacturing industries, data that characterize the manufacturing process are electronically collected and stored in the databases.Due to advances in data collection systems and analysis tools, data mining (DM)has widely been applied for quality assessment (QA) in manufacturing industries.In DM, the choice of technique to use in analyzing a dataset and assessing the quality depend on the understanding of the analyst. On the other hand, with the advent of improved and efficient prediction techniques, there is a need for an analyst to know which tool performs best for a particular type of data set.Although a few review papers have recently been published to discuss DM applications in manufacturing for QA, this paper provides an extensive review to investigate the application of a special DM technique, namely support vector machine (SVM) to solve QA problems. The review provides a comprehensive analysis of the literature from various points of view as DM preliminaries, data preprocessing, DM applications for each quality task, SVM preliminaries, and application results. Summary tables and figures are also provided besides to the analyses. Finally, conclusions and future research directions are provided.

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“…Due to the reason that IDS tries to identify whether the operation is attack or normal, many research studies have considered intrusion detection as a classification problem. The performance of the IDS model can be affected by lots of factors like redundant features or parameters of classifiers, which will lead to a low detection rate [6]. To address those problems, this article proposes an effective support vector machine (SVM) intrusion detection framework based on Tabu-Artificial Bee Colony (TABC) for feature subset selection and parameter optimization, named the TABC-SVM method.…”

Section: Introductionmentioning

confidence: 99%

Intrusion detection system based on improved abc algorithm with tabu search

Chen

Chang

et al. 2019

IEEJ Transactions Elec Engng

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An intrusion detection system (IDS) plays an important role in cyber security to detect network attacks. To improve the effectiveness of IDS, a new intrusion detection approach based on the support vector machine and improved Artificial Bee Colony algorithm (ABC) with Tabu Search (TS) is proposed. In the new method, to solve the problem of redundant network data and insufficient model parameters, a synchronous optimization strategy for feature selection and parameter calculation is proposed. At the same time, the idea of TS is a substitute for the greedy selection property of ABC, and two different selection probability formulas are provided in the early and later evolution of the ABC algorithm. The experimental results show that the newly proposed method performs better than other existing methods, especially in terms of detection accuracy and false negative rate. Besides, the detection rate of Probe attack and DoS attack reached 99.987 and 99.687%, respectively.

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Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy

Cited by 91 publications

References 23 publications

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

Review of data mining applications for quality assessment in manufacturing industry: support vector machines

Intrusion detection system based on improved abc algorithm with tabu search

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