PSO-Based Method for SVM Classification on Skewed Data-Sets

Cervantes, Jair; García-Lamont, Farid; López, Asdrúbal; Rodríguez, Lisbeth; Castilla, José Sergio Ruiz; Trueba, Adrián

doi:10.1007/978-3-319-22053-6_9

Cited by 16 publications

(10 citation statements)

References 9 publications

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“…The values selection is the same in all four above methods in order to make comparison fairly (e.g., iteration = 30 and Pop Size = 30). For the PSO, the parameters were fixed with the values given in the literature [22,26] (i.e., = 0:9, = 0:5, and = 1:25). For CMAES, the parameters were fixed with the values given in the literature [24] (i.e., = 0:25 and = 4 + 3log( )).…”

Section: Resultsmentioning

confidence: 99%

Optimizing SVM’s parameters based on backtracking search optimization algorithm for gear fault diagnosis

Thai¹,

Cheng²,

Nguyen³

et al. 2019

J. vibroeng.

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The accuracy of a support vector machine (SVM) classifier is decided by the selection of optimal parameters for the SVM. The Backtracking Search Optimization Algorithm (BSA) is often applied to resolve the global optimization problem and adapted to optimize SVM parameters. In this research, a SVM parameter optimization method based on BSA (BSA-SVM) is proposed, and the BSA-SVM is applied to diagnose gear faults. Firstly, a gear vibration signal can be decomposed into several intrinsic scale components (ISCs) by means of the Local Characteristics-Scale Decomposition (LCD). Secondly, the MPE can extract the fault feature vectors from the first few ISCs. Thirdly, the fault feature vectors are taken as the input vectors of the BSA-SVM classifier. The analysis results of BSA-SVM classifier show that this method has higher accuracy than GA (Genetic Algorithm) or PSO (Particles Swarm Algorithm) algorithms combined with SVM. In short, the BSA-SVM based on the MPE-LCD is suitable to diagnose the state of health gear.

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Section: Resultsmentioning

confidence: 99%

Optimizing SVM’s parameters based on backtracking search optimization algorithm for gear fault diagnosis

Thai¹,

Cheng²,

Nguyen³

et al. 2019

J. vibroeng.

View full text Add to dashboard Cite

show abstract

“…We classified our datasets using decision tree classification algorithm, which is called J48 in WEKA. The classification stage was conducting using 5-folds cross-validation to have enough positive minority class instances in every fold to minimize the data distribution problems [20], [21]. As we are dealing with unbalanced data set, evaluating the classifier with classification accuracy alone do not give a good overview about the classifier accuracy in predicting the minority class, instead a confusion matrix such as Table II is used for checking different metrics to get a good overview about the classifier prediction power [22].…”

Section: Discussionmentioning

confidence: 99%

AB-SMOTE: An Affinitive Borderline SMOTE Approach for Imbalanced Data Binary Classification

Majzoub¹,

Elgedawy²

2020

IJMLC

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SMOTE is an oversampling approach previously proposed to solve the imbalanced data binary classification problem. SMOTE managed to improve the classification accuracy, however it needs to generate large number of synthetic instances, which is not efficient in terms of memory and time. To overcome such drawbacks, the Borderline-SMOTE (BSMOTE) is previously proposed to minimize the number of generated synthetic instances by generating such instances based on the borderline between the majority and minority classes. Unfortunately, BSMOTE could not provide big savings regarding the number of generated instances, trading to the classification accuracy. To improve BSMOTE accuracy, this paper proposes an Affinitive Borderline SMOTE (AB-SMOTE) that leverages the BSMOTE, and improves the quality of the generated synthetic data by taking into consideration the affinity of the borderline instances. Experiments' results show the AB-SOMTE, when compared with BSMOTE, managed to produce the most accurate results in the majority of the test cases adopted in our study.

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“…In PSO, each particle can be represented as a candidate solution (position) in the search space. The particles fly through the search space by their own efforts and in cooperation with other particles and they follow the best solutions they have achieved (local best solutions) as well as tracking the best solutions that they found (the best global solution) (Cervantes et al, 2017;Lai et al, 2016;Mirjalili and Lewis, 2013;Wen et al, 2011).…”

Section: J Eng Applied Sci 15 (1): 310-318 2020mentioning

confidence: 99%

Selection of Tuning Parameter in L1-Support Vector Machine via. Particle Swarm Optimization Method

Al-Thanoon¹,

Qasim²,

Algamal³

2019

J. Eng. Appl. Sci.

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Descriptor selection for classification methods is one of the most important topics in the chemometrics. The selection of descriptors can be considered to be a variable selection problem that aims to find a small subset of descriptors that has the most discriminative information for the classification target. Penalized Support Vector Machine (PSVM) is one of the most effective embedded methods and it is more preferable than the Support Vector Machine (SVM) because PSVM combines the standard SVM with a penalty to simultaneously perform both variable selection and classification. The PSVM with L1-norm is the most widely used methods. However, the efficiency of PSVM with L1-norm depends on appropriately choosing the tuning parameter which is involved in the L1-norm penalty. In this study, a particle swarm optimization method which is a metaheuristic continuous algorithm is proposed to determine the tuning parameter in PSVM with L1-norm penalty. The proposed method will efficiently help to find the most significant descriptors in constructing Quantitative Structure-Activity Relationship classification (QSAR) model with high classification performance. Depend on the four datasets, the experimental results show the favorable performance of the proposed method when the number of descriptors is high and the sample size is low comparing with other competitor methods.

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PSO-Based Method for SVM Classification on Skewed Data-Sets

Cited by 16 publications

References 9 publications

Optimizing SVM’s parameters based on backtracking search optimization algorithm for gear fault diagnosis

Optimizing SVM’s parameters based on backtracking search optimization algorithm for gear fault diagnosis

AB-SMOTE: An Affinitive Borderline SMOTE Approach for Imbalanced Data Binary Classification

Selection of Tuning Parameter in L1-Support Vector Machine via. Particle Swarm Optimization Method

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