Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning
Artificial neural networks have achieved a great success in simulating the information processing mechanism and process of neuron supervised learning, such as classification. However, traditional artificial neurons still have many problems such as slow and difficult training. This paper proposes a n...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/3259222 |
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| _version_ | 1832558391406362624 |
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| author | Dongbao Jia Weixiang Xu Dengzhi Liu Zhongxun Xu Zhaoman Zhong Xinxin Ban |
| author_facet | Dongbao Jia Weixiang Xu Dengzhi Liu Zhongxun Xu Zhaoman Zhong Xinxin Ban |
| author_sort | Dongbao Jia |
| collection | DOAJ |
| description | Artificial neural networks have achieved a great success in simulating the information processing mechanism and process of neuron supervised learning, such as classification. However, traditional artificial neurons still have many problems such as slow and difficult training. This paper proposes a new dendrite neuron model (DNM), which combines metaheuristic algorithm and dendrite neuron model effectively. Eight learning algorithms including traditional backpropagation, classic evolutionary algorithms such as biogeography-based optimization, particle swarm optimization, genetic algorithm, population-based incremental learning, competitive swarm optimization, differential evolution, and state-of-the-art jSO algorithm are used for training of dendritic neuron model. The optimal combination of user-defined parameters of model has been systemically investigated, and four different datasets involving classification problem are investigated using proposed DNM. Compared with common machine learning methods such as decision tree, support vector machine, k-nearest neighbor, and artificial neural networks, dendritic neuron model trained by biogeography-based optimization has significant advantages. It has the characteristics of simple structure and low cost and can be used as a neuron model to solve practical problems with a high precision. |
| format | Article |
| id | doaj-art-738735a6600f4b26a3ad3973fcc476bc |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-738735a6600f4b26a3ad3973fcc476bc2025-02-03T01:32:29ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/3259222Verification of Classification Model and Dendritic Neuron Model Based on Machine LearningDongbao Jia0Weixiang Xu1Dengzhi Liu2Zhongxun Xu3Zhaoman Zhong4Xinxin Ban5School of Computer EngineeringSchool of Computer EngineeringSchool of Computer EngineeringSchool of Computer EngineeringSchool of Computer EngineeringSchool of Environmental and Chemical EngineeringArtificial neural networks have achieved a great success in simulating the information processing mechanism and process of neuron supervised learning, such as classification. However, traditional artificial neurons still have many problems such as slow and difficult training. This paper proposes a new dendrite neuron model (DNM), which combines metaheuristic algorithm and dendrite neuron model effectively. Eight learning algorithms including traditional backpropagation, classic evolutionary algorithms such as biogeography-based optimization, particle swarm optimization, genetic algorithm, population-based incremental learning, competitive swarm optimization, differential evolution, and state-of-the-art jSO algorithm are used for training of dendritic neuron model. The optimal combination of user-defined parameters of model has been systemically investigated, and four different datasets involving classification problem are investigated using proposed DNM. Compared with common machine learning methods such as decision tree, support vector machine, k-nearest neighbor, and artificial neural networks, dendritic neuron model trained by biogeography-based optimization has significant advantages. It has the characteristics of simple structure and low cost and can be used as a neuron model to solve practical problems with a high precision.http://dx.doi.org/10.1155/2022/3259222 |
| spellingShingle | Dongbao Jia Weixiang Xu Dengzhi Liu Zhongxun Xu Zhaoman Zhong Xinxin Ban Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning Discrete Dynamics in Nature and Society |
| title | Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning |
| title_full | Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning |
| title_fullStr | Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning |
| title_full_unstemmed | Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning |
| title_short | Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning |
| title_sort | verification of classification model and dendritic neuron model based on machine learning |
| url | http://dx.doi.org/10.1155/2022/3259222 |
| work_keys_str_mv | AT dongbaojia verificationofclassificationmodelanddendriticneuronmodelbasedonmachinelearning AT weixiangxu verificationofclassificationmodelanddendriticneuronmodelbasedonmachinelearning AT dengzhiliu verificationofclassificationmodelanddendriticneuronmodelbasedonmachinelearning AT zhongxunxu verificationofclassificationmodelanddendriticneuronmodelbasedonmachinelearning AT zhaomanzhong verificationofclassificationmodelanddendriticneuronmodelbasedonmachinelearning AT xinxinban verificationofclassificationmodelanddendriticneuronmodelbasedonmachinelearning |