A comparative study on grey relational analysis and C5.0 classification algorithm on adventitious rhizogenesis of Eucalyptus

Saha, Romeet; Ginwal, H. S.; Chandra, Girish; Barthwal, Santan

doi:10.1007/s00468-020-02008-4

Cited by 7 publications

(1 citation statement)

References 23 publications

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“…It is a network of neurons. Each neuron has only one connection, but the output connection can connect as many connections as needed, and these connections output the same signal, that is, the signal of the corresponding neuron, but the size of the signal is different and it varies with the number of branches [ 12 ]. According to the difference of network structure and learning algorithm, artificial neural network can be divided into multilayer perceptron, self-organizing map, and Hopfield network.…”

Section: Methodsmentioning

confidence: 99%

Attribute Feature Classification of English Grammar Entry Base Based on Support Vector Machine Classification Algorithm

Yinghua

Shaoxiu

Juan

2022

Computational Intelligence and Neuroscience

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An attribute feature classification method of English grammar vocabulary entry database based on support vector machine classification algorithm is proposed; this method takes news English as the research object and focuses on the classification of attributes and features of the English grammar lexicon database. First, the k-means algorithm is used to cluster the training set, and the one-to-many method is used to train two types of classifiers for the texts that cannot be correctly clustered in each class, that is, the classifiers of the corresponding categories are trained, and then the training set passed through a pair of the classifier generated by multiple SVMs is tested, and the samples that fall in the inseparable area are retrained by a one-to-one method, so as to achieve the purpose of balancing the training samples and reducing the inseparable area. The results show that, compared with the FDAGSVM algorithm, the proposed three multiclass classification algorithms have significantly improved classification speed and classification accuracy, and the macro average accuracy rates are 77.94%, 73.94%, and 72.36%, respectively. While ensuring the classification speed and classification accuracy of the single-label samples, the multiclass classification is realized, and it has high accuracy, recall rate, and value, which better solves the multiclass classification problem and expands the classification capability of the support vector machine. In addition, a comprehensive index based on the SVM classification algorithm is proposed to ensure the specialization of the attribute feature classification.

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Section: Methodsmentioning

confidence: 99%