Reliable Algorithms for Machine Learning Models: Implementation Research in Data Science
Creators
- 1. Business Intelligence Analyst, Schneider Electric, Bangalore, India.
- 2. Programmer Analyst, Cognizant Technology Solutions, Kolkata, India. Email: anukriti98@gmail.com
Contributors
Contact person:
- 1. Business Intelligence Analyst, Schneider Electric, Bangalore, India.
Description
Abstract: Machine Learning generates programs that make predictions and informed decisions about complex problems in an efficient and reliable way. These ML programs autonomously test solutions against the dataset to find the best fit for the problem. The paper aims to review the ML algorithms that develop prediction models by utilizing training dataset and known output. The paper also focuses on ML principles, algorithms, approaches, and applications for Supervised, Unsupervised, and Reinforcement learning that can perform tasks without being explicitly programmed for it. Completely opposite to rule-based programming, the machine learning paradigm uses examples of real data sets and pre-process it before providing the desired outputs based on these examples. In the case of more involved and complex tasks, it can be challenging for humans to explicitly program the models. On the other hand, it can be more effective to help the machines develop the algorithms for advanced tasks. This paper will also present the trending real-world applications of Machine Learning in Image Recognition and Biomedicine. Additionally, it will provide a background analysis of machine learning and related fields of data science.
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- Journal article: 2277-3878 (ISSN)
References
- M. Zhang and Z. Zhou, "A Review on Multi-Label Learning Algorithms" in IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. 08, pp. 1819-1837, 2014. doi: 10.1109/TKDE.2013.39
- Trends in extreme learning machines: a review, by Huang, G., Huang, G., Song, S., & You, K. (2015). Neural Networks, (HIC: 0 , CV: 0)
- A survey of multiple classifier systems as hybrid systems , by Corchado, E., Graña, M., & Wozniak, M. (2014). Information Fusion, 16, 3-17. ( HIC: 1 , CV: 22)
- solo-learn: A Library of Self-supervised Methods for Visual Representation Learning Victor Guilherme Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, Elisa Ricci, 2022.
- DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler; 23(53):1−6, 2022
- htps://www.sciencedirect.com/topics/engineering/machine-learningalgorithm
- https://machinelearningmastery.com/a-tour-of-machine-learningalgorithms/
- https://en.wikipedia.org/wiki/Machine_learning
- Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems Jayakumar Subramanian, Amit Sinha, Raihan Seraj, Aditya Mahajan, 2022
- http://places.csail.mit.edu/places_NIPS14.pdf
- K. Shailaja, B. Seetharamulu and M. A. Jabbar, "Machine Learning in Healthcare: A Review," 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018, pp.910-914, doi:10.1109/ICECA.2018.8474918
- Park, C., Took, C.C. & Seong, JK. Machine learning in biomedical engineering. Biomed. Eng. Lett. 8, 1–3 (2018). https://doi.org/10.1007/s13534-018-0058-3
- Evangelia I. Zacharaki; Sumei Wang; Sanjeev Chawla; Dong Soo Yoo; Ronald Wolf; Elias R. Melhem; Christos Davatzikos (2009). Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. 62(6), 1609–1618
- Sachdeva, Jainy, et al. "A dual neural network ensemble approach for multiclass brain tumor classification." International journal for numerical methods in biomedical engineering 28.11 (2012): 1107- 1120
- Xu, Y., Ju, L., Tong, J. et al. Machine Learning Algorithms for Predicting the Recurrence of Stage IV Colorectal Cancer After Tumor Resection. Sci Rep 10, 2519 (2020). https://doi.org/10.1038/s41598-020-59115-y
- Vamathevan, J., Clark, D., Czodrowski, P. et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 18, 463–477 (2019). https://doi.org/10.1038/s41573-019- 0024-5
- Moe Elbadawi, Simon Gaisford, Abdul W. Basit, Advanced machine-learning techniques in drug discovery, Drug Discovery Today, Volume 26, Issue 3, 2021, Pages 769-777,ISSN1359-6446, https://doi.org/10.1016/j.drudis.2020.12.003
- Lee, Cecilia S., and Aaron Y. Lee. "Clinical applications of continual learning machine learning." The Lancet Digital Health 2.6 (2020): e279-e281
- Bica, Ioana, et al. "From real‐world patient data to individualized treatment effects using machine learning: current and future methods to address underlying challenges." Clinical Pharmacology & Therapeutics 109.1 (2021): 87-100
Subjects
- ISSN: 2277-3878 (Online)
- https://portal.issn.org/resource/ISSN/2277-3878
- Retrieval Number: 100.1/ijrte.F68710310622
- https://www.ijrte.org/portfolio-item/F68710310622/
- Journal Website: www.ijrte.org
- https://www.ijrte.org/
- Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
- https://www.blueeyesintelligence.org/