A Hybrid Classification Model of Artificial Neural Network and Non Linear Kernel Support Vector Machine

Lena Anyango Onyango; Anthony Gichuhi Waititu; Thomas Mageto; Mutua Kilai

doi:doi:10.11648/j.ijdsa.20220802.15

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A Hybrid Classification Model of Artificial Neural Network and Non Linear Kernel Support Vector Machine

Lena Anyango Onyango, Anthony Gichuhi Waititu, Thomas Mageto, Mutua Kilai

Published in International Journal of Data Science and Analysis (Volume 8, Issue 2)

Received: 8 March 2022 Accepted: 28 March 2022 Published: 22 April 2022

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Abstract

Machine Learning Algorithms are employed in characterization, pattern recognition, and prediction. A hybrid model helps in reducing the computational complexity, improves accuracy, and results in an effective method for classification. The misclassification of the individual classifier is often excluded in a hybrid classifier. The objective of this research was to develop a hybrid classification model of Artificial Neural Network and non-linear kernel Support Vector Machine as an intelligent tool for achieving better classification performance and minimizing error rates. This study further evaluated the irreducibility and identifiability statistical properties of the ANN-SVM model. To achieve the hybridization of ANN and SVM, the research first obtained weights from the fitted Support Vector Machine model, and these weights were used as the initial weights in the Artificial Neural Network structure. The experiment was carried out in three distinct phases: selection of input features using the Boruta Wrapper Algorithm, classifier learning, and classifier combined effect and classification optimization. The study findings suggest that the hybrid ANN-SVM approach gives a higher performance accuracy of 89.7% and is more precise as compared to single ANN, SVM data mining algorithms. Therefore, the hybrid of ANN-SVM is the best binary classification system for classifying diabetes mellitus. The statistical software used for analysis was R.

Published in	International Journal of Data Science and Analysis (Volume 8, Issue 2)
DOI	10.11648/j.ijdsa.20220802.15
Page(s)	47-58
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Hybrid, Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification

References

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APA Style

Lena Anyango Onyango, Anthony Gichuhi Waititu, Thomas Mageto, Mutua Kilai. (2022). A Hybrid Classification Model of Artificial Neural Network and Non Linear Kernel Support Vector Machine. International Journal of Data Science and Analysis, 8(2), 47-58. https://doi.org/10.11648/j.ijdsa.20220802.15

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ACS Style

Lena Anyango Onyango; Anthony Gichuhi Waititu; Thomas Mageto; Mutua Kilai. A Hybrid Classification Model of Artificial Neural Network and Non Linear Kernel Support Vector Machine. Int. J. Data Sci. Anal. 2022, 8(2), 47-58. doi: 10.11648/j.ijdsa.20220802.15

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AMA Style

Lena Anyango Onyango, Anthony Gichuhi Waititu, Thomas Mageto, Mutua Kilai. A Hybrid Classification Model of Artificial Neural Network and Non Linear Kernel Support Vector Machine. Int J Data Sci Anal. 2022;8(2):47-58. doi: 10.11648/j.ijdsa.20220802.15

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@article{10.11648/j.ijdsa.20220802.15,
  author = {Lena Anyango Onyango and Anthony Gichuhi Waititu and Thomas Mageto and Mutua Kilai},
  title = {A Hybrid Classification Model of Artificial Neural Network and Non Linear Kernel Support Vector Machine},
  journal = {International Journal of Data Science and Analysis},
  volume = {8},
  number = {2},
  pages = {47-58},
  doi = {10.11648/j.ijdsa.20220802.15},
  url = {https://doi.org/10.11648/j.ijdsa.20220802.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20220802.15},
  abstract = {Machine Learning Algorithms are employed in characterization, pattern recognition, and prediction. A hybrid model helps in reducing the computational complexity, improves accuracy, and results in an effective method for classification. The misclassification of the individual classifier is often excluded in a hybrid classifier. The objective of this research was to develop a hybrid classification model of Artificial Neural Network and non-linear kernel Support Vector Machine as an intelligent tool for achieving better classification performance and minimizing error rates. This study further evaluated the irreducibility and identifiability statistical properties of the ANN-SVM model. To achieve the hybridization of ANN and SVM, the research first obtained weights from the fitted Support Vector Machine model, and these weights were used as the initial weights in the Artificial Neural Network structure. The experiment was carried out in three distinct phases: selection of input features using the Boruta Wrapper Algorithm, classifier learning, and classifier combined effect and classification optimization. The study findings suggest that the hybrid ANN-SVM approach gives a higher performance accuracy of 89.7% and is more precise as compared to single ANN, SVM data mining algorithms. Therefore, the hybrid of ANN-SVM is the best binary classification system for classifying diabetes mellitus. The statistical software used for analysis was R.},
 year = {2022}
}

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TY  - JOUR
T1  - A Hybrid Classification Model of Artificial Neural Network and Non Linear Kernel Support Vector Machine
AU  - Lena Anyango Onyango
AU  - Anthony Gichuhi Waititu
AU  - Thomas Mageto
AU  - Mutua Kilai
Y1  - 2022/04/22
PY  - 2022
N1  - https://doi.org/10.11648/j.ijdsa.20220802.15
DO  - 10.11648/j.ijdsa.20220802.15
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 47
EP  - 58
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20220802.15
AB  - Machine Learning Algorithms are employed in characterization, pattern recognition, and prediction. A hybrid model helps in reducing the computational complexity, improves accuracy, and results in an effective method for classification. The misclassification of the individual classifier is often excluded in a hybrid classifier. The objective of this research was to develop a hybrid classification model of Artificial Neural Network and non-linear kernel Support Vector Machine as an intelligent tool for achieving better classification performance and minimizing error rates. This study further evaluated the irreducibility and identifiability statistical properties of the ANN-SVM model. To achieve the hybridization of ANN and SVM, the research first obtained weights from the fitted Support Vector Machine model, and these weights were used as the initial weights in the Artificial Neural Network structure. The experiment was carried out in three distinct phases: selection of input features using the Boruta Wrapper Algorithm, classifier learning, and classifier combined effect and classification optimization. The study findings suggest that the hybrid ANN-SVM approach gives a higher performance accuracy of 89.7% and is more precise as compared to single ANN, SVM data mining algorithms. Therefore, the hybrid of ANN-SVM is the best binary classification system for classifying diabetes mellitus. The statistical software used for analysis was R.
VL  - 8
IS  - 2
ER  -

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Author Information

Lena Anyango Onyango

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Gichuhi Waititu

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Thomas Mageto

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Mutua Kilai

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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APA Style

Lena Anyango Onyango, Anthony Gichuhi Waititu, Thomas Mageto, Mutua Kilai. (2022). A Hybrid Classification Model of Artificial Neural Network and Non Linear Kernel Support Vector Machine. International Journal of Data Science and Analysis, 8(2), 47-58. https://doi.org/10.11648/j.ijdsa.20220802.15

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ACS Style

Lena Anyango Onyango; Anthony Gichuhi Waititu; Thomas Mageto; Mutua Kilai. A Hybrid Classification Model of Artificial Neural Network and Non Linear Kernel Support Vector Machine. Int. J. Data Sci. Anal. 2022, 8(2), 47-58. doi: 10.11648/j.ijdsa.20220802.15

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AMA Style

Lena Anyango Onyango, Anthony Gichuhi Waititu, Thomas Mageto, Mutua Kilai. A Hybrid Classification Model of Artificial Neural Network and Non Linear Kernel Support Vector Machine. Int J Data Sci Anal. 2022;8(2):47-58. doi: 10.11648/j.ijdsa.20220802.15

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@article{10.11648/j.ijdsa.20220802.15,
  author = {Lena Anyango Onyango and Anthony Gichuhi Waititu and Thomas Mageto and Mutua Kilai},
  title = {A Hybrid Classification Model of Artificial Neural Network and Non Linear Kernel Support Vector Machine},
  journal = {International Journal of Data Science and Analysis},
  volume = {8},
  number = {2},
  pages = {47-58},
  doi = {10.11648/j.ijdsa.20220802.15},
  url = {https://doi.org/10.11648/j.ijdsa.20220802.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20220802.15},
  abstract = {Machine Learning Algorithms are employed in characterization, pattern recognition, and prediction. A hybrid model helps in reducing the computational complexity, improves accuracy, and results in an effective method for classification. The misclassification of the individual classifier is often excluded in a hybrid classifier. The objective of this research was to develop a hybrid classification model of Artificial Neural Network and non-linear kernel Support Vector Machine as an intelligent tool for achieving better classification performance and minimizing error rates. This study further evaluated the irreducibility and identifiability statistical properties of the ANN-SVM model. To achieve the hybridization of ANN and SVM, the research first obtained weights from the fitted Support Vector Machine model, and these weights were used as the initial weights in the Artificial Neural Network structure. The experiment was carried out in three distinct phases: selection of input features using the Boruta Wrapper Algorithm, classifier learning, and classifier combined effect and classification optimization. The study findings suggest that the hybrid ANN-SVM approach gives a higher performance accuracy of 89.7% and is more precise as compared to single ANN, SVM data mining algorithms. Therefore, the hybrid of ANN-SVM is the best binary classification system for classifying diabetes mellitus. The statistical software used for analysis was R.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - A Hybrid Classification Model of Artificial Neural Network and Non Linear Kernel Support Vector Machine
AU  - Lena Anyango Onyango
AU  - Anthony Gichuhi Waititu
AU  - Thomas Mageto
AU  - Mutua Kilai
Y1  - 2022/04/22
PY  - 2022
N1  - https://doi.org/10.11648/j.ijdsa.20220802.15
DO  - 10.11648/j.ijdsa.20220802.15
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 47
EP  - 58
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20220802.15
AB  - Machine Learning Algorithms are employed in characterization, pattern recognition, and prediction. A hybrid model helps in reducing the computational complexity, improves accuracy, and results in an effective method for classification. The misclassification of the individual classifier is often excluded in a hybrid classifier. The objective of this research was to develop a hybrid classification model of Artificial Neural Network and non-linear kernel Support Vector Machine as an intelligent tool for achieving better classification performance and minimizing error rates. This study further evaluated the irreducibility and identifiability statistical properties of the ANN-SVM model. To achieve the hybridization of ANN and SVM, the research first obtained weights from the fitted Support Vector Machine model, and these weights were used as the initial weights in the Artificial Neural Network structure. The experiment was carried out in three distinct phases: selection of input features using the Boruta Wrapper Algorithm, classifier learning, and classifier combined effect and classification optimization. The study findings suggest that the hybrid ANN-SVM approach gives a higher performance accuracy of 89.7% and is more precise as compared to single ANN, SVM data mining algorithms. Therefore, the hybrid of ANN-SVM is the best binary classification system for classifying diabetes mellitus. The statistical software used for analysis was R.
VL  - 8
IS  - 2
ER  -

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