Teaching Learning Based Optimized Mathematical Model for Data Classification Problems

Babu, Preethitha; Satapathy, Suresh Chandra; Samantula, Mohan Krishna; Patra, Prashanta Kumar; Biswal, Bhabendra Narayan

doi:10.1007/978-3-642-35314-7_55

Cited by 3 publications

(2 citation statements)

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“…Reference [217] presents a new method for improving feature selection algorithm based on rough set theory using the TLBO algorithm. Moreover, [218][219][220] address the training of a feedforward neural network, a polynomial neural network and a specific type of high order-NN [221] called a pi-sigma neural network [222] with the help of evolutionary algorithms of TLBO, PSO, DE and GA. Based on the results, in all cases, neural network algorithms trained by TLBO created better results. One of the most important advantages of the TLBO algorithm is the high convergence rate [223].…”

Section: Teaching Learning Based Optimization (Tlbo)mentioning

confidence: 99%

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain–machine interface systems

2018

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Section: Teaching Learning Based Optimization (Tlbo)mentioning

confidence: 99%

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain–machine interface systems

2018

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“…where new, represents the improved learners, represents the current learners, is a random number in the interval [0, 1], 1 is the desired mean, mean is the current mean [42], and is a teaching factor that is not a parameter of the TLBO algorithm: it is calculated randomly using (14), which decides the value of the mean to be changed [43]:…”

mentioning

confidence: 99%

Heart Disease Diagnosis Utilizing Hybrid Fuzzy Wavelet Neural Network and Teaching Learning Based Optimization Algorithm

Alneamy

Alnaish

2014

Advances in Artificial Neural Systems

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Among the various diseases that threaten human life is heart disease. This disease is considered to be one of the leading causes of death in the world. Actually, the medical diagnosis of heart disease is a complex task and must be made in an accurate manner. Therefore, a software has been developed based on advanced computer technologies to assist doctors in the diagnostic process. This paper intends to use the hybrid teaching learning based optimization (TLBO) algorithm and fuzzy wavelet neural network (FWNN) for heart disease diagnosis. The TLBO algorithm is applied to enhance performance of the FWNN. The hybrid TLBO algorithm with FWNN is used to classify the Cleveland heart disease dataset obtained from the University of California at Irvine (UCI) machine learning repository. The performance of the proposed method (TLBO_FWNN) is estimated usingK-fold cross validation based on mean square error (MSE), classification accuracy, and the execution time. The experimental results show that TLBO_FWNN has an effective performance for diagnosing heart disease with 90.29% accuracy and superior performance compared to other methods in the literature.

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A Comparative Study Among Some Natural-Inspired Optimization Algorithms

Alneamy

Alnaish

2022

2022 8th International Conference on Contemporary Information Technology and Mathematics (ICCITM)

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Teaching Learning Based Optimized Mathematical Model for Data Classification Problems

Cited by 3 publications

References 5 publications

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain–machine interface systems

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain–machine interface systems

Heart Disease Diagnosis Utilizing Hybrid Fuzzy Wavelet Neural Network and Teaching Learning Based Optimization Algorithm

A Comparative Study Among Some Natural-Inspired Optimization Algorithms

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