1 |
C. M. Bishop, "Neural networks for pattern recognition," Oxford University Press, pp.365, 1996.
|
2 |
H. Y. Lee, "Research methodology," 2nd ed. Seoul, Korea: CRbooks, pp.234-235, 2014.
|
3 |
C. Ayuya, "Ensemble learning on bias and variance," Updated on January 20, 2021, Section [Internet], https://www.section.io/engineering-education/ensemble-bias-var/
|
4 |
D. H. Yang, K. M. Ngoc, I. S. Shin, K. H. Lee, and M. G. Hwang, "Ensemble-based out-of-distribution detection," Electronics, Vol.10, Iss.5, 2021.
|
5 |
F. Mohareb, O. Papadopoulou, and E. Panagou, "Ensemblebased support vector machine classifiers as an efficient tool for quality assessment of beef fillets from electronic nose data," Analytical Methods, Vol.8, Iss.18, pp.3711-3721, 2016.
DOI
|
6 |
C. Jian, J. Gao, and Y. Ao, "A new sampling method for classifying imbalanced data based on support vector machine ensemble," Neurocomputing, Vol.193, Iss.C, pp.115-122, 2016.
DOI
|
7 |
S. Bulusu, B. Kailkhura, P. K. Varshney, B. Li, and D. Song, "Anomalous example detection in deep learning: A survey," IEEE Access, Vol.8, pp.132330-132347, 2020.
DOI
|
8 |
Y. B. Hur, E. H. Yang, and S. J. Hwang, "A simple framework for robust out-of-distribution detection," IEEE Access, Vol.10, pp.23086-23097, 2022.
DOI
|
9 |
D. Hendrycks and K. Gimpel, "A baseline for detecting misclassified and out-of-distribution examples in neural networks," International Conference on Learning Representations, 2017.
|
10 |
S. Liang, Y. Li, and R. Srikant, "Enhancing the reliability of Out-of-Distribution image detection in neural networks," International Conference on Learning Representations, 2018.
|
11 |
K. Hansson, S. Yella, M. Doughherty, and H. Fleyeh, "Machine learning algorithms in heavy process manufacturing," American Journal of Intelligent Systems, Vol.6, No.1, pp.1-6, 2016.
|
12 |
D. Hendrycks, M. Mazeika, and T. Dietterich, "Deep anomaly detection with outlier exposure," International Conference on Learning Representations, 2019.
|
13 |
E. A. Zanaty, "Support Vector Machines (SVMs) versus Multilayer Perceptron (MLP) in data classification," Egyptian Informatics Journal, Vol.13, Iss.3, pp.177-183, 2012.
DOI
|
14 |
Y. Liu, A. An, and X. Huang, "Boosting prediction accuracy on imbalanced datasets with SVM ensembles," 10th Pacific-Asia Conference, Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp.107-118, 2006.
|
15 |
K. M. Lee, H. L. Lee, K. B. Lee, and J. W. Shin, "Training confidence-calibrated classifiers for detecting Out-ofDistribution samples," International Conference on Learning Representations, 2018.
|
16 |
M. Hossin and M. N. Sulaiman, "A review on evaluation metrics for data classification evaluations," International Journal of Data Mining & Knowledge Management Process, Vol.5, No.2, pp.1-11, 2015.
|
17 |
M. Sensoy, L. Kaplan, and M. Kandemir, "Evidential deep learning to quantify classification uncertainty," Neural Information Processing Systems, 2018.
|
18 |
M. Farrash and W. Wang, "How data partitioning strategies and subset size influence the performance of an ensemble?," IEEE International Conference on Big Data, pp.42-49, 2013.
|
19 |
S. M. Nzuva, L. Nderu, and T. Mwalili, "Ensemble model for enhancing classification accuracy in intrusion detection systems," International Conference on Electrical, Computer and Energy Technologies, 2021.
|