Determine the Kernel Parameter of KFDA Using a Minimum Search Algorithm

Classification model of support vector machine (SVM) overcomes the problem of a big number of samples. But the kernel parameter and the punishment factor have great influence on the quality of SVM model. Particle swarm optimization (PSO) is an evolutionary search algorithm based on the swarm intelligence, which is suitable for parameter optimization. Accordingly, a nonlinear inertia convergence classification model (NICCM) is proposed after the nonlinear inertia convergence (NICPSO) is developed in this paper. The velocity of NICPSO is firstly defined as the weighted velocity of the inertia PSO, and the inertia factor is selected to be a nonlinear function. NICPSO is used to optimize the kernel parameter and a punishment factor of SVM. Then, NICCM classifier is trained by using the optical punishment factor and the optical kernel parameter that comes from the optimal particle. Finally, NICCM is applied to the classification of the normal state and fault states of online power cable. It is experimentally proved that the iteration number for the proposed NICPSO to reach the optimal position decreases from 15 to 5 compared with PSO; the training duration is decreased by 0.0052 s and the recognition precision is increased by 4.12% compared with SVM.

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GSA to Obtain SVM Kernel Parameter for Thyroid Nodule Classification

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ◽

10.22146/ijccs.41215 ◽

2020 ◽

Vol 14 (1) ◽

pp. 11

Author(s):

Dias Aziz Pramudita ◽

Aina Musdholifah

Keyword(s):

Thyroid Nodule ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Classification Performance ◽

Polynomial Kernel ◽

Support Vector ◽

Classification Problems ◽

Kernel Parameter ◽

Global Optima ◽

Law Of Gravity

Support Vector Machine (SVM) is one of the most popular methods of classification problems due to its global optima solution. However, the selection of appropriate parameters and kernel values remains an obstacle in the process. The problem can be solved by adding the best value of parameter during optimization process in SVM. Gravitational Search Algorithm (GSA) will be used to optimize parameters of SVM. GSA is an optimization algorithm that is inspired by mass interaction and Newton's law of gravity. This research hybridizes the GSA and SVM to increase system accuracy. The proposed approach had been implemented to improve the classification performance of Thyroid Nodule. The data used in this research are ultrasonography image of Thyroid Nodule obtained from RSUP Dr. Sardjito, Yogyakarta. This research had been evaluated by comparing the default SVM parameters with the proposed method in term of accuracy. The experiment results showed that the use of GSA on SVM is capable to increase system accuracy. In the polynomial kernel the accuracy rose up from 58.5366 % to 89.4309 %, and 41.4634 % to 98.374 % in Polynomial kernel

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Rolling bearing fault diagnosis based on MEEMD sample entropy and SSA-SVM

Journal of Physics Conference Series ◽

10.1088/1742-6596/2125/1/012003 ◽

2021 ◽

Vol 2125 (1) ◽

pp. 012003

Author(s):

Xuguang Li ◽

Liyou Fu

Keyword(s):

Fault Diagnosis ◽

Search Algorithm ◽

Recognition Rate ◽

Rolling Bearing ◽

Ensemble Empirical Mode Decomposition ◽

Sample Entropy ◽

Permutation Entropy ◽

Support Vector ◽

Svm Classifier ◽

Kernel Parameter

Abstract The penalty parameter (c) and kernel parameter (g) contained in Support Vector Machine (SVM) cannot be adaptively selected according to actual samples, which results in low classification accuracy and slow convergence speed. A novel sparrow search algorithm was used to optimize the parameters of SVM classifier. Firstly, an improved ensemble empirical mode decomposition (MEEMD) method was used to decompose non-stationary and nonlinear vibration signals, and the eigenmode function (IMF) was obtained by removing abnormal signals from the original signals through permutation entropy, and the sample entropy was extracted. Finally, a fault diagnosis model based on SSA-SVM is constructed, and the high recognition rate and effectiveness of this method are proved by simulation and experimental data analysis.

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Fast search algorithm for VQ-based recognition of isolated words

IEE Proceedings I Communications Speech and Vision ◽

10.1049/ip-i-2.1989.0059 ◽

1989 ◽

Vol 136 (6) ◽

pp. 391 ◽

Cited By ~ 15

Author(s):

S.H. Chen ◽

J.S. Pan

Keyword(s):

Search Algorithm ◽

Fast Search ◽

Fast Search Algorithm

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Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189134 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8125-8137

Author(s):

Jackson J Christy ◽

D Rekha ◽

V Vijayakumar ◽

Glaucio H.S. Carvalho

Keyword(s):

Intelligent Transportation System ◽

Search Algorithm ◽

Mac Protocol ◽

High Mobility ◽

Optimal Solution ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Traffic Density ◽

Scheduling Problem ◽

Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

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Comparison of Meta-heuristic Algorithms on Benchmark Functions

Academic Perspective Procedia ◽

10.33793/acperpro.02.03.41 ◽

2019 ◽

Vol 2 (3) ◽

pp. 508-517

Author(s):

FerdaNur Arıcı ◽

Ersin Kaya

Keyword(s):

Heuristic Algorithms ◽

Optimization Problems ◽

Search Algorithm ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Differential Evolution Algorithm ◽

Population Based ◽

Time Interval ◽

Bee Colony ◽

Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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