Improving Multi-Class Classification on 5-Celebrity-Faces Dataset using Ensemble Classification Methods
Abstract
This study aims to compare the performance between Random Forest Classifier and Gaussian Naïve Bayes Classifier in classification. Several evaluation metrics such as accuracy, precision, recall, and F1-score were used to analyze the performance of both models. The dataset used has specific characteristics that influence the evaluation results. The research findings indicate that Random Forest Classifier outperforms Gaussian Naïve Bayes Classifier in most of the evaluation metrics. Random Forest Classifier achieves higher accuracy and better precision, recall, and weighted F1-score. However, it should be noted that Random Forest Classifier also has more outliers compared to Gaussian Naïve Bayes Classifier when visualized using boxplots. Therefore, in selecting a classification model, a trade-off between higher performance and sensitivity to outliers needs to be considered. Further statistical testing and advanced evaluation are required to gain a deeper understanding of the impact and interpretation of the obtained results. This study provides valuable insights into understanding the comparison between these two classification models and their implications in different contexts.
Downloads
References
P. Himthani, P. Gurbani, K. D. Raghuwanshi, and ..., “Ordered Ensemble Classifier Chain for Image and Emotion Classification,” Congress on Intelligent …, 2022, doi: 10.1007/978-981-16-9416-5_28.
D. F. H. Permadi and M. Z. Abdullah, “Implementasi Pengenalan Citra Wajah Binatang Dengan Algoritma Convolutional Neural Network (CNN),” juti.if.its.ac.id, [Online]. Available: http://juti.if.its.ac.id/index.php/juti/article/view/1131
Y. Hou, T. Chen, X. Lun, and F. Wang, “A novel method for classification of multi-class motor imagery tasks based on feature fusion,” Neurosci Res, 2022, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0168010221002042
S. R. S. Chakravarthy and H. Rajaguru, “Performance analysis of ensemble classifiers and a two-level classifier in the classification of severity in digital mammograms,” Soft comput, 2022, doi: 10.1007/s00500-022-07273-8.
L. Odenthal, J. Allmer, and M. Yousef, “Ensemble classifiers for multiclass microRNA classification,” miRNomics: MicroRNA Biology and …, 2022, doi: 10.1007/978-1-0716-1170-8_12.
N. Gul, S. Ahmed, S. M. Kim, and J. Kim, “Improved Sensing Performance with Autoencoder and Ensemble Classifier,” 2022 27th Asia Pacific …, 2022, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9943610/
M. Kumar, K. Bajaj, B. Sharma, and S. Narang, “A Comparative Performance Assessment of Optimized Multilevel Ensemble Learning Model with Existing Classifier Models,” Big Data, 2022, doi: 10.1089/big.2021.0257.
M. V Subbarao, G. C. Ram, D. G. Kumar, and ..., “Brain Tumor Classification using Ensemble Classifiers,” … on Electronics and …, 2022, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9752177/
J. Rosid, D. M. Sakti, W. S. Murti, and ..., “Face recognition dengan metode Haar Cascade dan Facenet,” Indonesian Journal of …, 2022, [Online]. Available: https://jurnal.yoctobrain.org/index.php/ijodas/article/view/38
S. Ma, G. Ahn, and H. Hong, “Chest CT image patch-based CNN classification and visualization for predicting recurrence of non-small cell lung Cancer patients,” Journal of the Korea Computer Graphics …, 2022, [Online]. Available: http://journal.cg-korea.org/download/download_bib?pid=jkcgs-28-1-1
D. Mohan, V. Ulagamuthalvi, N. Joseph, and ..., “Patient-Specific Brain Tumor Segmentation using Hybrid Ensemble Classifier to Extract Deep Features,” International Journal of …, 2023, [Online]. Available: https://www.ijisae.org/index.php/IJISAE/article/view/2579
S. C. K. Tékouabou, I. Chabbar, H. Toulni, W. Cherif, and ..., “Optimizing the early glaucoma detection from visual fields by combining preprocessing techniques and ensemble classifier with selection strategies,” Expert Systems with …, 2022, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0957417421013257
B. Kolukisa and B. Bakir-Gungor, “Ensemble feature selection and classification methods for machine learning-based coronary artery disease diagnosis,” Computer Standards &Interfaces, 2023, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0920548922000733
A. Subasi and S. M. Qaisar, “Surface EMG signal classification using TQWT, Bagging and Boosting for hand movement recognition,” Journal of Ambient Intelligence and Humanized …, 2022, doi: 10.1007/s12652-020-01980-6.
Q. Gu, X. Zhang, L. Chen, and N. Xiong, “An improved bagging ensemble surrogate-assisted evolutionary algorithm for expensive many-objective optimization,” Applied Intelligence, 2022, doi: 10.1007/s10489-021-02709-4.
A. Das, S. K. Mohapatra, and M. N. Mohanty, “Brain Image Classification Using Optimized Extreme Gradient Boosting Ensemble Classifier,” Biologically Inspired Techniques in …, 2022, doi: 10.1007/978-981-16-8739-6_20.
F. T. Admojo and Y. I. Sulistya, “Analisis performa algoritma Stochastic Gradient Descent (SGD) dalam mengklasifikasi tahu berformalin,” Indonesian Journal of Data and …, 2022, [Online]. Available: https://jurnal.yoctobrain.org/index.php/ijodas/article/view/42
R. Wang, K. Y. Li, and Y. Su, “Prediction of ameloblastoma recurrence using random forest—a machine learning algorithm,” International Journal of Oral and Maxillofacial …, 2022, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0901502721004094
B. Imran, H. Hambali, A. Subki, and ..., “Data Mining Using Random Forest, Naïve Bayes, and Adaboost Models for Prediction and Classification of Benign and Malignant Breast Cancer,” Jurnal Pilar Nusa …, 2022, [Online]. Available: http://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/2912
K. R. Bhatele and S. S. Bhadauria, “Multiclass classification of central nervous system brain tumor types based on proposed hybrid texture feature extraction methods and ensemble learning,” Multimed Tools Appl, 2023, doi: 10.1007/s11042-022-13439-1.
R. Ferdiana, “New Approach of Ensemble Method to Improve Performance of IDS using S-SDN Classifier,” 2022 IEEE International Conference on …, 2022, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9994302/
É. Bédard, V. D. B. de Vazelhes, and G. Beaudoin, “Performance of predictive supervised classification models of trace elements in magnetite for mineral exploration,” Journal of Geochemical …, 2022, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0375674222000176
A. Nugroho and A. Husin, “Performance Analysis of Random Forest Using Attribute Normalization,” SISTEMASI, 2022, [Online]. Available: http://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/1681
A. N. A. Thohari, L. Triyono, I. Hestiningsih, and ..., “Performance Evaluation of Pre-Trained Convolutional Neural Network Model for Skin Disease Classification,” JUITA: Jurnal …, 2022, [Online]. Available: http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/12041
H. Hamdani, H. R. Hatta, N. Puspitasari, and ..., “Dengue classification method using support vector machines and cross-validation techniques,” … Journal of Artificial …, 2022, [Online]. Available: https://search.proquest.com/openview/a607c8361a7aac70dfc0dabf2b63f41b/1?pqorigsite=gscholar&cbl=1686339
F. Arévalo, M. T. Ibrahim, M. P. C. Alison, and A. Schwung, “Anomaly Detection using Ensemble Classification and Evidence Theory,” IEEE Access, 2023, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10135082/
B. S. Bhati, A. Shankar, S. Saxena, and ..., “An ensemble-based approach for image classification using voting classifier,” International …, 2022, doi: 10.1504/IJMIC.2022.127099.
G. Antariksa, R. Muammar, and J. Lee, “Performance evaluation of machine learning-based classification with rock-physics analysis of geological lithofacies in Tarakan Basin, Indonesia,” Journal of Petroleum Science and …, 2022, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0920410521009050
Copyright (c) 2023 Indonesian Journal of Data and Science
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- The work is not under consideration for publication elsewhere.
- The work has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with Indonesian Journal of Data and Science agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. (CC BY-NC 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
