Cross-Domain Learning Frameworks for Enterprise Decision Systems
Abstract
: In building decision systems in different areas of operation, enterprises are often faced with disjointed data environments and a lack of labeled datasets. This paper presents a cross Genre learning system which will help in reuse of knowledge in the various industries and problem domains and to solve the problems of risk management as well as maintaining trust. The framework suggested incorporates transferable representations, domain adaptation methods and check methods to counter risk of negative transfer. A major concern is to ensure that models and decision logic can be compatible with governance structures, can be interpreted and deployed in a controlled way in an environment other than where they are used. The framework focuses on the feasibility of cross-domain learning in hastening innovation within the enterprise, maintaining the principle of reliability and responsibility as the decision-making procedure. Using cross-domain learning, companies can rise above constraints of data and silos of operation, and more quickly and effectively find answers to complicated issues. The article highlights the necessity of striking a balance between the necessity of flexibility in the application of the model and strict governance and interpretability in order to promote the responsible application of the enterprise decision systems
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 7 No. 3 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
14059-14068 |
Published |
June 13, 2024 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Sumanth Reddy Anumula (2024). Cross-Domain Learning Frameworks for Enterprise Decision Systems. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 7 No. 3 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 14059-14068. https://doi.org/10.15662/IJAESIT.2024.0703003 |
References
2. Yaroslav Ganin and Victor Lempitsky, “Unsupervised Domain Adaptation by Backpropagation,” in Proc. of the 32nd Int. Conf. on Machine Learning, vol. 37, 2015, pp. 1180–1189.
3. P. Nancy et al., “Detection of brain tumour using machine learning based framework by classifying MRI images,” International Journal of Nanotechnology, vol. 20, no. 5/6/7/8/9/10, pp. 880–896, Jan. 2023, doi: https://doi.org/10.1504/ijnt.2023.134040.
4. Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky, “Domain-adversarial training of neural networks,” The Journal of Machine Learning Research, vol. 17, no. 1, pp. 2096–2030, 2016.
5. Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan, “Learning Transferable Features with Deep Adaptation Networks,” in Proc. of the 32nd Int. Conf. on Machine Learning, vol. 37, 2015, pp. 97–105.
6. Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I. Jordan, “Conditional Adversarial Domain Adaptation,” in Proc. of the 32nd Int. Conf. on Neural Information Processing Systems, 2018, pp. 1647–1657.
7. Mengda Huang, Yang Liu, Xiang Ao, Kuan Li, Jianfeng Chi, Jinghua Feng, Hao Yang, and Qing He, “AUC-Oriented Graph Neural Network for Fraud Detection,” in Proc. of the ACM Web Conference, 2022, pp. 1311–1321.
8. Jesse Davis and Mark Goadrich, “The Relationship between Precision-Recall and ROC Curves,” in Proc. of the 23rd Int. Conf. on Machine Learning, 2006, pp. 233–240.
9. Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell, “Deep Domain Confusion: Maximizing for Domain Invariance,” CoRR, abs/1412.3474, 2014. [Online]. Available: http://arxiv.org/abs/1412.3474.
10. Chao Song, Youfang Lin, Shengnan Guo, and Huaiyu Wan, “Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting,” Proc. of the AAAI Conf. on Artificial Intelligence, vol. 34, no. 01, pp. 914–921, 2020.