Novel hybrid SVM-TLBO forecasting model incorporating dimensionality reduction techniques

Das, Shom Prasad; Achary, N. Sangita; Padhy, Sudarsan

doi:10.1007/s10489-016-0801-3

Cited by 17 publications

(10 citation statements)

References 23 publications

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“…In this study, the teaching-learningbased optimization (TLBO) algorithm presented by Rao et al [65] is employed which does not consist of any algorithm-specific parameter. This method mimics the betterment of students' performance in a classroom by teaching and learning and has been employed to solve diverse engineering problems [78,79]. In the first stage of a single iteration, the teacher improves the students by teaching, and in the second phase, students learn from any other student in a classroom.…”

Section: Optimization Algorithm For Damage Detection: Tlbomentioning

confidence: 99%

Teaching–learning-based optimization algorithm for solving structural damage detection problem in frames via changes in vibration responses

Kundu

Mishra

Maity

2021

Archit. Struct. Constr.

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Optimization algorithms are primarily responsible for efficiency in vibration-based damage detection particularly when utilizing the inverse approach. A complex problem of damage detection tends to converge into local minima, generated by a false damaged state which produces a response that is almost similar to the actual damaged state. Hence, there is a need for an efficient and accurate soft computing technique that can find the global minima or the actual damaged state. Recently, the teaching-learning based optimization (TLBO) algorithm has become quite popular due to its superior performance especially when compared to other metaheuristic algorithms. In this paper, damage estimation capability of the TLBO for frame structures and a benchmark problem of cantilever beam is studied and comparisons are made with some established soft computing techniques. TLBO is observed to produce better results relative to the other artificial intelligence-based techniques used for structural health monitoring.

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Section: Optimization Algorithm For Damage Detection: Tlbomentioning

confidence: 99%

Teaching–learning-based optimization algorithm for solving structural damage detection problem in frames via changes in vibration responses

Kundu

Mishra

Maity

2021

Archit. Struct. Constr.

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“…[16] planned a two step fusion approach with SVR model perform better results than a single stage ANN with random forest model. Das et al [17] proposed an ensemble model named DR-SVM-TLBO for predicting the energy commodity futures index in financial time series. This model used PCA, KPCA, and ICA for performing the dimensionality reduction and SVM-TLBO hybrid component for prediction on the reduced features.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Jaffel, I. et al [18] analyzed that moving window reduced kernel principal component analysis MW-RKPCA are perform better results than and MW-KPCA applied to monitoring the nonlinear dynamic system. Das, S. P. [19] designed a hybrid model of DR-SVM-TLBO and applied different dimension reduction method i.e PCA, KPCA and ICA to reduce the features of supplied data. Patel, V. K. and Savsani, V. J.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Stock Index of Tata Steel using Hybrid Machine Learning Based Optimization Techniques

Siddique¹,

Panda²

2019

IJRTE

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The trend of stock price prediction has always been in the focal point of analytical activity in financial domain for both the researchers and investors. Prediction with accuracy is very essential for improved investment decisions that imbibe minimum risk factors. Due to this, majority of investors depend upon that intelligent trading system which generates better forecasting results. As forecasting stock market price with high accuracy is quite a challenging task for the analysts, machine learning has been adopted as one of the popular techniques to predict future trends. Even if there are many recognized analytical time series analysis that are categorized either under soft computing or under conventional statistical techniques like fuzzy logic, artificial neural networks and genetic algorithms, researchers have been looking for more appropriate techniques which can exhibit improved results. In this paper, we developed different hybrid machine learning based prediction models and compared their efficiency. Dimension reduction techniques such as orthogonal forward selection (OFS) and kernel principal component analysis (KPCA) are used separately with support vector regression (SVR) and teaching learning based optimization (TLBO) to predict the stock price of Tata Steel. The performance of both the proposed approach is evaluated with 4143days daily transactional data of Tata steels stocks price, which was collected from Bombay Stock Exchange (BSE). We compared the results of both OFS-SVR-TLBO and KPCA-SVR-TLBO hybrid models and concludes that by incorporating KPCA is more practicable and performs better results than OFS

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“…However, the dimension elevation can greatly increase the computational complexity, the display expression of nonlinear mapping can not be determined in some situations. How to find the most suitable dimension is the direction of the optimization of SVM algorithm by relevant researchers in the past decade or so [30].…”

Section: Related Workmentioning

confidence: 99%

Prediction of Marine Pycnocline Based on Kernel Support Vector Machine and Convex Optimization Technology

Yang

Liu

Zhang

et al. 2019

Sensors

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With the explosive growth of ocean data, it is of great significance to use ocean observation data to analyze ocean pycnocline data in military field. However, due to natural factors, most of the time the ocean hydrological data is not complete. In this case, predicting the ocean hydrological data by partial data has become a hot spot in marine science. In this paper, based on the traditional statistical analysis literature, we propose a machine-learning ocean hydrological data processing process under big data. At the same time, based on the traditional pycnocline gradient determination method, the open Argo data set is analyzed, and the local characteristics of pycnocline are verified from several aspects combined with the current research about pycnocline. Most importantly, in this paper, the combination of kernel function and support vector machine(SVM) is extended to nonlinear learning by using the idea of machine learning and convex optimization technology. Based on this, the known pycnocline training set is trained, and an accurate model is obtained to predict the pycnocline in unknown domains. In the specific steps, this paper combines the classification problem with the regression problem, and determines the proportion of training set and test formula set by polynomial regression. Subsequently, the feature scaling of the input data accelerated the gradient convergence, and a grid search algorithm with variable step size was proposed to determine the super parameter c and gamma of the SVM model. The prediction results not only used the confusion matrix to analyze the accuracy of GridSearch-SVM with variable step size, but also compared the traditional SVM and the similar algorithm. At the end of the experiment, two features which have the greatest influence on the Marine density thermocline are found out by the feature ranking algorithm based on learning.

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Novel hybrid SVM-TLBO forecasting model incorporating dimensionality reduction techniques

Cited by 17 publications

References 23 publications

Teaching–learning-based optimization algorithm for solving structural damage detection problem in frames via changes in vibration responses

Teaching–learning-based optimization algorithm for solving structural damage detection problem in frames via changes in vibration responses

Prediction of Stock Index of Tata Steel using Hybrid Machine Learning Based Optimization Techniques

Prediction of Marine Pycnocline Based on Kernel Support Vector Machine and Convex Optimization Technology

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