<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Due to the different classification effects and accuracy of different classification algorithms in machine learning, it is inconvenient for scientific researchers to choose which classification algorithm to use. This paper uses the face data published by Cambridge University as an experiment. The experiment first reduces the dimensionality of the data through the principal component analysis (PCA) algorithm, extracts the main features of the data, and then respectively through linear logic classification, linear discrimination LDA, nearest neighbor algorithm KNN, support vector machine SVM and the integrated algorithm Adaboost are used for classification. By comparing the advantages and disadvantages of the classification performance and complexity of different algorithms, the final review reviews accuracy, recall, f1-score, and AUC as evaluation indicators.</p></abstract>

Due to the different classification effects and accuracy of different classification algorithms in machine learning, it is inconvenient for scientific researchers to choose which classification algorithm to use. This paper uses the face data published by Cambridge University as an experiment. The experiment first reduces the dimensionality of the data through the principal component analysis (PCA) algorithm, extracts the main features of the data, and then respectively through linear logic classification, linear discrimination LDA, nearest neighbor algorithm KNN, support vector machine SVM and the integrated algorithm Adaboost are used for classification. By comparing the advantages and disadvantages of the classification performance and complexity of different algorithms, the final review reviews accuracy, recall, f1-score, and AUC as evaluation indicators.

A Comparative Study of Face Recognition Classification Algorithms

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"A Comparative Study of Face Recognition Classification Algorithms"}

Due to the different classification effects and accuracy of different classification algorithms in machine learning, it is inconvenient for scientific...

	PCA+LR	PCA+LDA	PCA+SVM	PCA+KNN	PCA+NB
Precision (%)	99	98	94	59	91
Recall (%)	99	96	91	45	85
Fl-score (%)	99	96	91	44	85

A Comparative Study of Face Recognition Classification Algorithms

Online veröffentlicht: 14. Okt. 2020

Seitenbereich: 23 - 29

DOI: https://doi.org/10.21307/ijanmc-2020-024

Schlüsselwörter
Classification Algorithm, Machine Learning, Face Recognition, Model Evaluation

© 2020 Changyuan Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

THE COMPARISON OF EXPERIMENTAL RESULTS OF FIVE CLASSIFICATION ALGORITHMS

A Comparative Study of Face Recognition Classification Algorithms

Online veröffentlicht: 14. Okt. 2020

Seitenbereich: 23 - 29

DOI: https://doi.org/10.21307/ijanmc-2020-024

SchlüsselwörterClassification Algorithm, Machine Learning, Face Recognition, Model Evaluation

© 2020 Changyuan Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

THE COMPARISON OF EXPERIMENTAL RESULTS OF FIVE CLASSIFICATION ALGORITHMS

Schlüsselwörter
Classification Algorithm, Machine Learning, Face Recognition, Model Evaluation