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Shehadeh HA. Chernobyl disaster optimizer (CDO): a novel meta-heuristic method for global optimization. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08261-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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2
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Yang R, Wang P, Qi J. A novel SSA-CatBoost machine learning model for credit rating. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-221652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Categorical Boost (CatBoost) is a new approach in credit rating. In the process of classification and prediction using CatBoost, parameter tuning and feature selection are two crucial parts, which affect the classification accuracy of CatBoost significantly. This paper proposes a novel SSA-CatBoost model, which mixes Sparrow Search Algorithm (SSA) and CatBoost to improve classification and prediction accuracy for credit rating. In terms of parameter tuning, the SSA-CatBoost optimization obtains the most optimal parameters by iterating and updating the sparrow’s position, and utilize the optimal parameter to improve the accuracy of classification and prediction. In terms of feature selection, a novel wrapping method called Recursive Feature Elimination algorithm is adopted to reduce the adverse impact of noise data on the results, and further improves calculation efficiency. To evaluate the performance of the proposed SSA-CatBoost model, P2P lending datasets are employed to assess the prediction results, then the interpretable Shap package is used to explain the reason why the proposed model considers a sample as good or bad. Consequently, the experimental results show that the SSA-CatBoost model has an ideal accuracy in classification and prediction for credit rating by comparing the SSA-CatBoost model with the CatBoost model and other well-known machine learning models.
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Affiliation(s)
- Ruicheng Yang
- Finance School, Inner Mongolia University of Finance and Economics, Hohhot, Inner Mongolia, China
| | - Pucong Wang
- Finance School, Inner Mongolia University of Finance and Economics, Hohhot, Inner Mongolia, China
| | - Ji Qi
- Finance School, Inner Mongolia University of Finance and Economics, Hohhot, Inner Mongolia, China
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3
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Boosted Sine Cosine Algorithm with Application to Medical Diagnosis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:6215574. [PMID: 35785140 PMCID: PMC9242811 DOI: 10.1155/2022/6215574] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 05/19/2022] [Accepted: 05/26/2022] [Indexed: 01/09/2023]
Abstract
The sine cosine algorithm (SCA) was proposed for solving optimization tasks, of which the way to obtain the optimal solution is mainly through the continuous iteration of the sine and cosine update formulas. However, SCA also faces low population diversity and stagnation of locally optimal solutions. Hence, we try to eliminate these problems by proposing an enhanced version of SCA, named ESCA_PSO. ESCA_PSO is proposed based on hybrid SCA and particle swarm optimization (PSO) by incorporating multiple mutation strategies into the original SCA_PSO. To validate the effect of ESCA_PSO in handling global optimization problems, ESCA_PSO was compared with quality algorithms on various types of benchmark functions. In addition, the proposed ESCA_PSO was employed to tune the best parameters of support vector machines for dealing with medical diagnosis tasks. The results prove the efficiency of the proposed algorithms in solving optimization problems.
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Ding Y, Yan Y, Li J, Chen X, Jiang H. Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM. Foods 2022; 11:foods11111658. [PMID: 35681408 PMCID: PMC9180160 DOI: 10.3390/foods11111658] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/27/2022] [Accepted: 05/31/2022] [Indexed: 02/04/2023] Open
Abstract
In this paper, we propose a method for classifying tea quality levels based on near-infrared spectroscopy. Firstly, the absorbance spectra of Huangshan Maofeng tea samples were obtained in a wavenumber range of 10,000~4000 cm−1 using near-infrared spectroscopy. The spectral data were then converted to transmittance and smoothed using the Savitzky–Golay (SG) algorithm. The denoised transmittance spectra were dimensionally reduced using principal component analysis (PCA). The characteristic variables obtained using PCA were used as the input variables and the tea level was used as the output to establish a support vector machine (SVM) classification model. The penalty factor c and the kernel function parameter g in the SVM model were optimized using particle swarm optimization (PSO) and comprehensive-learning particle swarm optimization (CLPSO) algorithms. The final experimental results show that the CLPSO-SVM method had the best classification performance, and the classification accuracy reached 99.17%.
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Affiliation(s)
- Yuhan Ding
- Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, China; (Y.D.); (J.L.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
- Institute of High-Performance Electrical Machine System and Intelligent Control, Jiangsu University, Zhenjiang 212013, China
| | - Yuli Yan
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Y.Y.); (X.C.)
| | - Jun Li
- Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, China; (Y.D.); (J.L.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
- School of Automation, Southeast University, Nanjing 210096, China
| | - Xu Chen
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Y.Y.); (X.C.)
| | - Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Y.Y.); (X.C.)
- Correspondence:
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Ding X, Yang F, Ma F. An Efficient Model Selection for Linear Discrimination Function-based Recursive Feature Elimination. J Biomed Inform 2022; 129:104070. [DOI: 10.1016/j.jbi.2022.104070] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 04/02/2022] [Accepted: 04/05/2022] [Indexed: 11/30/2022]
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Evolutionary competitive swarm exploring optimal support vector machines and feature weighting. Soft comput 2021. [DOI: 10.1007/s00500-020-05439-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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8
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Multi-Strategy Ensemble Whale Optimization Algorithm and Its Application to Analog Circuits Intelligent Fault Diagnosis. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10113667] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The whale optimization algorithm (WOA) is a new swarm intelligence (SI) optimization algorithm, which has the superiorities of fewer parameters and stronger searching ability. However, previous studies have indicated that there are shortages in maintaining diversity and avoiding local optimal solutions. This paper proposes a multi-strategy ensemble whale optimization algorithm (MSWOA) to alleviate these deficiencies. First, the chaotic initialization strategy is performed to enhance the quality of the initial population. Then, an improved random searching mechanism is designed to reduce blindness in the exploration phase and speed up the convergence. In addition, the original spiral updating position is modified by the Levy flight strategy, which leads to a better tradeoff between local and global search. Finally, an enhanced position revising mechanism is utilized to improve the exploration further. To testify the superiorities of the proposed MSWOA algorithm, a series of comparative experiments are carried out. On the one hand, the numerical optimization experimental results, which are conducted under nineteen widely used benchmark functions, indicate that the performance of MSWOA stands out compared with the standard WOA and six other well-designed SI algorithms. On the other hand, MSWOA is utilized to tune the parameters of the support vector machine (SVM), which is applied to the fault diagnosis of analog circuits. Experimental results confirm that the proposed method has higher diagnosis accuracy than other competitors. Therefore, the MSWOA is successfully applied as a novel and efficient optimization algorithm.
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A Hybrid Fault Diagnosis Approach for Rotating Machinery with the Fusion of Entropy-Based Feature Extraction and SVM Optimized by a Chaos Quantum Sine Cosine Algorithm. ENTROPY 2018; 20:e20090626. [PMID: 33265715 PMCID: PMC7513146 DOI: 10.3390/e20090626] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2018] [Revised: 08/13/2018] [Accepted: 08/18/2018] [Indexed: 11/28/2022]
Abstract
As crucial equipment during industrial manufacture, the health status of rotating machinery affects the production efficiency and device safety. Hence, it is of great significance to diagnose rotating machinery faults, which can contribute to guarantee the running stability and plan for maintenance, thus promoting production efficiency and economic benefits. For this purpose, a hybrid fault diagnosis model with entropy-based feature extraction and SVM optimized by a chaos quantum sine cosine algorithm (CQSCA) is developed in this research. Firstly, the state-of-the-art variational mode decomposition (VMD) is utilized to decompose the vibration signals into sets of components, during which process the preset parameter K is confirmed with the central frequency observation method. Subsequently, the permutation entropy values of all components are computed to constitute the feature vectors corresponding to different kind of signals. Later, the newly developed sine cosine algorithm (SCA) is employed and improved with chaotic initialization by a Duffing system and quantum technique to optimize the support vector machine (SVM) model, with which the fault pattern is recognized. Additionally, the availability of the optimized SVM with CQSCA was revealed in pattern recognition experiments. Finally, the proposed hybrid fault diagnosis approach was employed for engineering applications as well as contrastive analysis. The comparative results show that the proposed method achieved the best training accuracy 99.5% and best testing accuracy 97.89%. Furthermore, it can be concluded from the boxplots of different diagnosis methods that the stability and precision of the proposed method is superior to those of others.
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Javidi MM, Zarisfi Kermani F. Utilizing the advantages of both global and local search strategies for finding a small subset of features in a two-stage method. APPL INTELL 2018. [DOI: 10.1007/s10489-018-1159-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Sayed GI, Darwish A, Hassanien AE. A new chaotic multi-verse optimization algorithm for solving engineering optimization problems. J EXP THEOR ARTIF IN 2018. [DOI: 10.1080/0952813x.2018.1430858] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Gehad Ismail Sayed
- Faculty of Computers and Information, Cairo University, Cairo, Egypt
- Scientific Research Group in Egypt, Egypt
| | - Ashraf Darwish
- Faculty of Science, Helwan University, Cairo, Egypt
- Scientific Research Group in Egypt, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Information, Cairo University, Cairo, Egypt
- Scientific Research Group in Egypt, Egypt
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Aljarah I, Al-Zoubi AM, Faris H, Hassonah MA, Mirjalili S, Saadeh H. Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm. Cognit Comput 2018. [DOI: 10.1007/s12559-017-9542-9] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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13
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Özyön S, Yaşar C, Temurtaş H. Incremental gravitational search algorithm for high-dimensional benchmark functions. Neural Comput Appl 2018. [DOI: 10.1007/s00521-017-3334-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Barik RK, Priyadarshini R, Dash N. A Meta-Heuristic Model for Data Classification Using Target Optimization. INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING 2017. [DOI: 10.4018/ijamc.2017070102] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The paper contains an extensive experimental study which focuses on a major idea on Target Optimization (TO) prior to the training process of artificial machines. Generally, during training process of an artificial machine, output is computed from two important parameters i.e. input and target. In general practice input is taken from the training data and target is randomly chosen, which may not be relevant to the corresponding training data. Hence, the overall training of the neural network becomes inefficient. The present study tries to put forward TO as an efficient methodology which may be helpful in addressing the said problem. The proposed work tries to implement the concept of TO and compares the outcomes with the conventional classifiers. In this regard, different benchmark data sets are used to compare the effect of TO on data classification by using Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) optimization techniques.
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Affiliation(s)
| | - Rojalina Priyadarshini
- Department of Information Technology, C. V. Raman College of Engineering, Bhubaneswar, India
| | - Nilamadhab Dash
- Department of Information Technology, C. V. Raman College of Engineering, Bhubaneswar, India
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Luo M, Li C, Zhang X, Li R, An X. Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings. ISA TRANSACTIONS 2016; 65:556-566. [PMID: 27622428 DOI: 10.1016/j.isatra.2016.08.022] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Revised: 07/30/2016] [Accepted: 08/22/2016] [Indexed: 06/06/2023]
Abstract
This paper proposes a hybrid system named as HGSA-ELM for fault diagnosis of rolling element bearings, in which real-valued gravitational search algorithm (RGSA) is employed to optimize the input weights and bias of ELM, and the binary-valued of GSA (BGSA) is used to select important features from a compound feature set. Three types fault features, namely time and frequency features, energy features and singular value features, are extracted to compose the compound feature set by applying ensemble empirical mode decomposition (EEMD). For fault diagnosis of a typical rolling element bearing system with 56 working condition, comparative experiments were designed to evaluate the proposed method. And results show that HGSA-ELM achieves significant high classification accuracy compared with its original version and methods in literatures.
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Affiliation(s)
- Meng Luo
- School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China
| | - Chaoshun Li
- School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China.
| | - Xiaoyuan Zhang
- College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, PR China
| | - Ruhai Li
- School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China
| | - Xueli An
- China Institute of Water Resources and Hydropower Research, Beijing 100044, PR China
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A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine. PLoS One 2016; 11:e0161259. [PMID: 27551829 PMCID: PMC4995046 DOI: 10.1371/journal.pone.0161259] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Accepted: 08/02/2016] [Indexed: 11/26/2022] Open
Abstract
Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.
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Heidari AA, Ali Abbaspour R, Rezaee Jordehi A. An efficient chaotic water cycle algorithm for optimization tasks. Neural Comput Appl 2015. [DOI: 10.1007/s00521-015-2037-2] [Citation(s) in RCA: 105] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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