Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification
Open accessAbstract
This paper presents a comparative account of unsupervised and supervised learning models and their pattern classification evaluations as applied to the higher education scenario. Classification plays a vital role in machine based learning algorithms and in the present study, we found that, though the error back-propagation learning algorithm as provided by supervised learning model is very efficient for a number of non-linear real-time problems, KSOM of unsupervised learning model, offers efficient solution and classification in the present study.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Multi-source collaborative QC contributes positively to the quality improvement of hydrological precipitation data.
“Furthermore, multi-source collaborative QC also contributes positively to the quality improvement of hydrological precipitation data [30,31].”
From A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data (2025) · cites this paper as [31]
Cited by
Sorted by citations
Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles
de Prado, M., Rusci, M., Capotondi, A., Donze, R., Benini, L., & Pazos, N. (2021) · Sensors · doi:10.3390/s21041339 · Cited by 57 · see citing works
Breath VOC analysis and machine learning approaches for disease screening: a review
P, H., Rangarajan, M., & Pandya, H. J. (2023) · Journal of Breath Research · doi:10.1088/1752-7163/acb283 · Cited by 37 · see citing works
Bringing Machine Learning Classifiers Into Critical Cyber-Physical Systems: A Matter of Design
Sayin, B., Zoppi, T., Marchini, N., Khokhar, F. A., & Passerini, A. (2025) · IEEE Access · doi:10.1109/access.2025.3568501 · Cited by 7 · see citing works
A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data
Sun, H., Zhou, Q., Shi, L., Li, C., Qin, S., Yao, D., Xu, M., Huang, Y., Hu, Q., & Guan, Y. (2025) · Remote Sensing · doi:10.3390/rs17243976 · Cited by 4 · see citing works
- Authors
- Sathya, R., & Abraham, A.
- Affiliations
- Bangalore University (IN)
- Venue
- International Journal of Advanced Research in Artificial Intelligence
- Published
- 2013
- Volume
- 2
- Issue
- 2
- ISSN
- 2165-4050 (ISSN-L)
- 2165-4069
- OpenAlex
- W2119018277
- Cited by
- 459
- References
- 0
- Citations by year
- 2026: 10 · 2025: 20 · 2024: 44 · 2023: 64 · 2022: 70 · 2021: 80 · 2020: 63 · 2019: 37 · 2018: 22 · 2017: 12 · 2016: 9 · 2015: 15 · 2014: 13
- FWCI
- 13.64
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ARTIFICIAL INTELLIGENCE (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- supervised learning, unsupervised learning, pattern classification, error back-propagation, self-organizing maps, higher education, algorithm comparison
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
