Multiobjective optimization of support vector regression parameters by teaching-learning-based optimization for modeling of electric discharge machining responses

Optimization algorithms are widely used for the identification of intrusion. This is attributable to the increasing number of audit data features and the decreasing performance of human-based smart Intrusion Detection Systems (IDS) regarding classification accuracy and training time. In this paper, an improved method for intrusion detection for binary classification was presented and discussed in detail. The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). The process of selecting the least number of features without any effect on the result accuracy in FSS was considered a multi-objective optimization problem. The NTLBO was proposed in this paper as an FSS mechanism; its algorithm-specific, parameter-less concept (which requires no parameter tuning during an optimization) was explored. The experiments were performed on the prominent intrusion machine-learning datasets (KDDCUP’99 and CICIDS 2017), where significant enhancements were observed with the suggested NTLBO algorithm as compared to the classical Teaching-Learning-Based Optimization algorithm (TLBO), NTLBO presented better results than TLBO and many existing works. The results showed that NTLBO reached 100% accuracy for KDDCUP’99 dataset and 97% for CICIDS dataset

Optimisation of Support Vector Regression Parameters

NIR news ◽

10.1255/nirn.898 ◽

2006 ◽

Vol 17 (4) ◽

pp. 12-13

Author(s):

Bülent Üstün ◽

Willem Melssen ◽

Lutgarde Buydens

Keyword(s):

Support Vector Regression ◽

Support Vector ◽

Regression Parameters

A Processing Predictive Model of Ultrasonic Vibration Grinding Assisted Electric Discharge Machining Based on Support Vector Machines

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.941-944.1928 ◽

2014 ◽

Vol 941-944 ◽

pp. 1928-1931

Author(s):

Guang Mei Yang ◽

Yun Peng Zhang ◽

Kai Yue Li ◽

Guo Ding Chen

Keyword(s):

Predictive Model ◽

Ultrasonic Vibration ◽

Electric Discharge ◽

Orthogonal Test ◽

Support Vector ◽

Maximum Relative Error ◽

Test Results ◽

Electric Discharge Machining ◽

Good Accord ◽

Vector Machines

To predict the machining results of ultrasonic vibration grinding assisted electric discharge machining (UVGAEDM) in the condition of building predictive model with a few samples but fluctuant values, a predictive model based on SVM was proposed in this paper. Taking machining SiCp/Al as an example, the samples for modeling were obtained through orthogonal test, and then the predictive model was established utilizing MATLAB. Finally, the model was optimized to further improve the prediction accuracy about the processing indicatorssurface roughness and processing velocity. It shows that the predictive results are in good accord with the test results, with the maximum relative error being less than 12%, meaning the predictive model is reliable and effective.

Optimization of Support Vector Regression Parameters by Flower Pollination Algorithm

Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017) ◽

10.2991/fmsmt-17.2017.313 ◽

2017 ◽

Author(s):

Yuan Yang ◽

Zhongqi Wang ◽

Bo Yang ◽

Xudong Liu

Keyword(s):

Support Vector Regression ◽

Flower Pollination Algorithm ◽

Support Vector ◽

Flower Pollination ◽

Regression Parameters

Effective Hybrid Algorithm of Taguchi Method, FEM, RSM, and Teaching Learning-Based Optimization for Multiobjective Optimization Design of a Compliant Rotary Positioning Stage for Nanoindentation Tester

Mathematical Problems in Engineering ◽

10.1155/2019/4191924 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16 ◽

Cited By ~ 3

Author(s):

Minh Phung Dang ◽

Thanh-Phong Dao ◽

Ngoc Le Chau ◽

Hieu Giang Le

Keyword(s):

Multiobjective Optimization ◽

Taguchi Method ◽

Safety Factor ◽

Optimization Design ◽

Hybrid Approach ◽

Numerical Data ◽

Hybrid Optimization Algorithm ◽

Positioning Stage ◽

Teaching Learning Based Optimization ◽

Teaching Learning

This paper proposes an effective hybrid optimization algorithm for multiobjective optimization design of a compliant rotary positioning stage for indentation tester. The stage is created with respect to the Beetle’s profile. To meet practical demands of the stage, the geometric parameters are optimized so as to find the best performances. In the present work, the Taguchi method is employed to lay out the number of numerical experiments. Subsequently, the finite element method is built to retrieve the numerical data. The mathematical models are then established based on the response surface method. Before conducting the optimization implementation, the weight factor of each response is calculated exactly. Based on the well-established models, the multiple performances are simultaneously optimized utilizing the teaching learning-based optimization. The results found that the weight factors of safety factor and displacement are 0.5995 (59.95%) and 0.4005 (40.05%), respectively. The results revealed that the optimal safety factor is about 1.558 and the optimal displacement is 2.096 mm. The validations are in good agreement with the predicted results. Sensitivity analysis is carried out to identify the effects of variables on the responses. Using the Wilcoxon’s rank signed test and Friedman test, the effectiveness of the proposed hybrid approach is better than that of other evolutionary algorithms. It ensures a good effectiveness to solve a complex multiobjective optimization problem.

Hybrid support vector regression and crow search algorithm for modeling and multiobjective optimization of microalgae-based wastewater treatment

Journal of Environmental Management ◽

10.1016/j.jenvman.2021.113783 ◽

2022 ◽

Vol 301 ◽

pp. 113783

Author(s):

S.M. Zakir Hossain ◽

Nahid Sultana ◽

M. Ezzudin Mohammed ◽

Shaikh A. Razzak ◽

Mohammad M. Hossain

Keyword(s):

Wastewater Treatment ◽

Multiobjective Optimization ◽

Support Vector Regression ◽

Search Algorithm ◽

Support Vector

Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

Journal of Control Science and Engineering ◽

10.1155/2017/3614790 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 10

Author(s):

Hailun Wang ◽

Daxing Xu

Keyword(s):

Kalman Filter ◽

Support Vector Regression ◽

Kernel Function ◽

Unscented Kalman Filter ◽

Selection Method ◽

Parameter Selection ◽

Support Vector ◽

Regression Parameters ◽

Cubature Kalman Filter ◽

Adaptive Fusion

Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF) support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.

A Hybrid Prediction Model of Kernel Principal Component Analysis, Support Vector Regression and Teaching Learning Based Optimization Techniques

Current Journal of Applied Science and Technology ◽

10.9734/cjast/2021/v40i2031460 ◽

2021 ◽

pp. 17-25

Author(s):

Mohammed Siddique ◽

Tumbanath Samantara ◽

Siba Prasad Mishra

Keyword(s):

Principal Component Analysis ◽

Stock Market ◽

Prediction Model ◽

Stock Price ◽

Principal Component ◽

Component Analysis ◽

Kernel Principal Component Analysis ◽

Support Vector ◽

Teaching Learning Based Optimization ◽

Teaching Learning

Forecasting of stock market is considered as one of the most decisive and critical tasks for the data scientists in financial domain. Stock market is one of exciting and demanding monetary activities for individual investors, and financial analysts. The stock market is an inter-connected important economic international business. Prediction of stock price has become a crucial issue for stock investors and brokers. The stock market is able to influence the day to day life of the common people. The stock price is based on the state of market stability. As the dormant high noises in the data impair the performance, reducing the noise would be competent while constructing the forecasting model. To achieve this task, integration of kernel principal component analysis, support vector machine with teaching learning based optimization algorithm is proposed in this research work. Kernel principal component analysis is able to remove the unnecessary and unrelated factors, and reduces the dimension of input variables and time complexity. The feasibility and efficiency of this proposed hybrid model has been applied to forecast the daily open prices of stock index of a leading Company. The performance of the proposed approach is evaluated with 3543 daily transactional (13th December 2001 to 4th December 2020) stocks price data from Bombay Stock Exchange (BSE). Empirical results show that the proposed model enhances the performance of the prediction model and can be used for taking better decision and more accurate predictions for financial investors.