Overall
- Language
- korean
- Conflict of Interest
- In relation to this article, we declare that there is no conflict of interest.
- Publication history
-
Received July 11, 2023
Accepted July 19, 2023
-
This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Most Cited
신경 망의 지도 학습을 위한 로그 간격의 학습 자료 구성 방식과 손실 함수의 성능 평가
Performance Evaluation of Loss Functions and Composition Methods of Log-scale Train Data for Supervised Learning of Neural Network
Download PDF
Abstract
지도 학습 기반의 신경 망을 활용한 공학적 자료의 분석은 화학공학 공정 최적화, 미세 먼지 농도 추정, 열역학적 상
평형 예측, 이동 현상 계의 물성 예측 등 다양한 분야에서 활용되고 있다. 신경 망의 지도 학습은 학습 자료를 요구하
며, 주어진 학습 자료의 구성에 따라 학습 성능이 영향을 받는다. 빈번히 관찰되는 공학적 자료 중에는 DNA의 길이,
분석 물질의 농도 등과 같이 로그 간격으로 주어지는 자료들이 존재한다. 본 연구에서는 넓은 범위에 분포된 로그 간
격의 학습 자료를 기계 학습으로 처리하는 경우, 사용 가능한 손실 함수들의 학습 성능을 정량적으로 평가하였으며, 적
합한 학습 자료 구성 방식을 연구하였다. 이를 수행하고자, 100×100의 가상 이미지를 활용하여 기계 학습의 회귀 과
업을 구성하였다. 4개의 손실 함수들에 대하여 (i) 오차 행렬, (ii) 최대 상대 오차, (iii) 평균 상대 오차로 정량적 평가
하여, mape 혹은 msle가 본 연구에서 다룬 과업에 대해 최적의 손실 함수가 됨을 알아내었다. 또한, 학습 자료의 값이
넓은 범위에 걸쳐 분포하는 경우, 학습 자료의 구성을 로그 간격 등을 고려하여 균등 선별하는 방식이 높은 학습 성능
을 보임을 밝혀내었다. 본 연구에서 다룬 회귀 과업은 DNA의 길이 예측, 생체 유래 분자 분석, 콜로이드 용액의 농도
추정 등의 공학적 과업에 적용 가능하며, 본 결과를 활용하여 기계 학습의 성능과 학습 효율의 증대를 기대할 수 있을
것이다.
The analysis of engineering data using neural network based on supervised learning has been utilized in
various engineering fields such as optimization of chemical engineering process, concentration prediction of particulate
matter pollution, prediction of thermodynamic phase equilibria, and prediction of physical properties for transport
phenomena system. The supervised learning requires training data, and the performance of the supervised learning is
affected by the composition and the configurations of the given training data. Among the frequently observed
engineering data, the data is given in log-scale such as length of DNA, concentration of analytes, etc. In this study, for
widely distributed log-scaled training data of virtual 100×100 images, available loss functions were quantitatively
evaluated in terms of (i) confusion matrix, (ii) maximum relative error and (iii) mean relative error. As a result, the loss
functions of mean-absolute-percentage-error and mean-squared-logarithmic-error were the optimal functions for the logscaled training data. Furthermore, we figured out that uniformly selected training data lead to the best prediction
performance. The optimal loss functions and method for how to compose training data studied in this work would be
applied to engineering problems such as evaluating DNA length, analyzing biomolecules, predicting concentration of
colloidal suspension.
References
2. Li, J., Zhang, M. and Wang, D., “Adaptive Demodulator Using Machine Learning for Orbital Angular Momentum Shift Keying,” IEEE Photonics Technology Letters, 29, 1455-1458(2017).
3. Poort, J. P., Ramdin, M., van Kranendonk, J. and Vlugt, T. J. H.,“Solving Vapor-liquid Flash Problems Using Artificial Neural Networks,” Fluid Phase Equilibria, 490, 39-47(2019).
4. Valderrama, J. O., Reátegui, A. and Rojas, R. E., “Density of Ionic Liquids Using Group Contribution and Artificial Neural Networks,” Industrial & Engineering Chemistry Research, 48, 3254-3259(2009).
5. Han, X., Wang, J. and Sun, Y., “Circulating Tumor DNA as Biomarkers for Cancer Detection,” Genomics, Proteomics & Bioinformatics, 15, 59-72(2017).
6. Lee, Y., El Andaloussi, S. and Wood, M. J., “Exosomes and Microvesicles: Extracellular Vesicles for Genetic Information Transfer and Gene Therapy,” Human molecular genetics, 21,R125-134(2012).
7. Baskaran, S., Panner Selvam, M. K. and Agarwal, A. in Advances in Clinical Chemistry Vol. 95 (ed Gregory S. Makowski) 149-163 (Elsevier, 2020).
8. Spada, S. and Galluzzi, L. Extracellular Vesicles. (Elsevier Science, 2020).
9. Goodfellow, I., Bengio, Y. and Courville, A. Deep Learning.(MIT Press, 2016).
10. Aldrees, A. et al., “Multi-Expression Programming (MEP): Water Quality Assessment Using Water Quality Indices,” Water, 14, 947(2022).
11. Alpan, K. and Sekeroglu, B., “Prediction of Pollutant Concentrations by Meteorological Data Using Machine Learning Algorithms,” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.,XLIV-4/W3-2020, 21-27(2020).
12. Cai, R. et al., “Prediction of Surface Chloride Concentration of Marine Concrete Using Ensemble Machine Learning,” Cement
and Concrete Research, 136, 106164(2020).
13. Bozdağ, A., Dokuz, Y. and Gökçek, Ö. B., “Spatial Prediction of PM10 Concentration Using Machine Learning Algorithms in Ankara, Turkey,” Environmental Pollution, 263, 114635(2020).
14. Sadhukhan, B., Chakraborty, S. and Mukherjee, S., “Predicting the Magnitude of An Impending Earthquake Using Deep Learning Techniques,” Earth Science Informatics, 16, 803-823(2023).
15. Bronkhorst, A. J., Ungerer, V. and Holdenrieder, S., “Comparison of Methods for the Isolation of Cell-free DNA from Cell Culture Supernatant,” Tumour Biology, 42(4), 1010428320916314(2020).
16. Theel, E. K. and Schwaminger, S. P., “Microfluidic Approaches for Affinity-Based Exosome Separation,” International Journal of Molecular Sciences, 23, 9004(2022).
17. Descamps, L., Le Roy, D. and Deman, A. L., “Microfluidic-Based Technologies for CTC Isolation: A Review of 10 Years of Intense Efforts towards Liquid Biopsy,” Int. J. Mol. Sci., 23(4), 1981(2022).
