DOI : https://doi.org/10.18517/ijaseit.14.4.20191

Challenges in Supervised and Unsupervised Learning: A Comprehensive Overview

Mohammed Tuays Almuqati (1), Fatimah Sidi (2), Siti Nurulain Mohd Rum (3), Maslina Zolkepli (4), Iskandar Ishak (5)
(1) Department of Computer Science, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang, Malaysia
(2) Department of Computer Science, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang, Malaysia
(3) Department of Computer Science, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang, Malaysia
(4) Department of Computer Science, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang, Malaysia
(5) Department of Computer Science, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang, Malaysia
Fulltext View | Download
How to cite (IJASEIT) :
Almuqati , Mohammed Tuays, et al. “Challenges in Supervised and Unsupervised Learning: A Comprehensive Overview”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 4, Aug. 2024, pp. 1449-55, doi:10.18517/ijaseit.14.4.20191.
Citation Format :
Data science and machine learning are at the forefront of modern technological advancements, promising automated insights, predictions, and decision-making. Supervised and unsupervised learning are pivotal paradigms within this dynamic landscape, each presenting its unique challenges. This article provides a comprehensive overview of the multifaceted challenges inherent to both supervised and unsupervised learning. This article reviews research studies published between 2019 and 2023. This article discusses the challenges of supervised and unsupervised learning. In supervised learning, challenges include data labeling, overfitting, limited generalization, and balancing mistake equivalence and decision-making goals. In unsupervised learning, difficulties encompass issues like overfitting, choosing the appropriate algorithm, and interpreting results. This includes evaluating the quality of clustering, deciding the optimal number of clusters, and managing noise and outliers. The article aims to provide insights into these challenges, enhancing the understanding of machine learning for both novices and experts. Researchers and practitioners constantly evolve their methods and tools to overcome these complexities. This article is a valuable reference for researchers and experts in the field, empowering them to navigate these challenges confidently. As technology advances, a thorough understanding of these challenges is essential for unlocking the full potential of these powerful tools. Finally, several recommendations were given to guide future researchers in applying machine learning in the journey of data-driven discovery and automation, offering challenges and opportunities for those who embark on it.

M. L. Brodie, “What Is Data Science?,” Applied Data Science, pp. 101–130, 2019, doi: 10.1007/978-3-030-11821-1_8.

A. Jain, Y. Sarma, K. Kishor , “Financial supervision and management system using Ml algorithm”. Solid State Technology, vol 63 no.6, pp.18974–18984, 2020.

P. B. Pires and J. D. Santos, “Artificial Intelligence and Marketing,” Confronting Security and Privacy Challenges in Digital Marketing, pp. 95–118, Jun. 2023, doi: 10.4018/978-1-6684-8958-1.ch005.

V. Bindu and C. Thomas, “Significance of Feature Selection and Pruning Algorithms in Machine Learning Classification of E-Mails,” Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities, pp. 29–50, 2021, doi:10.1007/978-3-030-72236-4_2.

U. Cali, M. Kuzlu, M. Pipattanasomporn, J. Kempf, and L. Bai, “Foundations of Big Data, Machine Learning, and Artificial Intelligence and Explainable Artificial Intelligence,” Digitalization of Power Markets and Systems Using Energy Informatics, pp. 115–137, 2021, doi: 10.1007/978-3-030-83301-5_6.

I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN computer science, Mar. 2021, doi:10.20944/preprints202103.0216.v1.

X. Wu, L. Xiao, Y. Sun, J. Zhang, T. Ma, and L. He, “A survey of human-in-the-loop for machine learning,” Future Generation Computer Systems, vol. 135, pp. 364–381, Oct. 2022, doi:10.1016/j.future.2022.05.014.

C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electronic Markets, vol. 31, no. 3, pp. 685–695, Apr. 2021, doi: 10.1007/s12525-021-00475-2.

G. Ciaburro and G. Iannace, “Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review,” Data, vol. 6, no. 6, p. 55, May 2021, doi: 10.3390/data6060055.

P. C. Sen, M. Hajra, and M. Ghosh, “Supervised Classification Algorithms in Machine Learning: A Survey and Review,” Emerging Technology in Modelling and Graphics, pp. 99–111, Jul. 2019, doi:10.1007/978-981-13-7403-6_11.

J. Wexler, M. Pushkarna, T. Bolukbasi, M. Wattenberg, F. Viegas, and J. Wilson, “The What-If Tool: Interactive Probing of Machine Learning Models,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–1, 2019, doi: 10.1109/tvcg.2019.2934619.

X. Xie, J. W. K. Ho, C. Murphy, G. Kaiser, B. Xu, and T. Y. Chen, “Testing and validating machine learning classifiers by metamorphic testing,” Journal of Systems and Software, vol. 84, no. 4, pp. 544–558, Apr. 2011, doi: 10.1016/j.jss.2010.11.920.

A. Paleyes, R.-G. Urma, and N. D. Lawrence, “Challenges in Deploying Machine Learning: A Survey of Case Studies,” ACM Computing Surveys, vol. 55, no. 6, pp. 1–29, Dec. 2022, doi:10.1145/3533378.

A. Aldelemy and Raed A. Abd-Alhameed, “Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning,” Journal of Techniques, vol. 5, no. 2, pp. 163–186, Jun. 2023, doi:10.51173/jt.v5i2.1226.

G. Vidhya, D. Nirmala, and T. Manju, “Quality challenges in Deep Learning Data Collection in perspective of Artificial Intelligence,” Journal of Information Technology and Computing, vol. 4, no. 1, pp. 46–58, Jun. 2023, doi: 10.48185/jitc.v4i1.725.

H. Nugroho, “A Review: Data Quality Problem in Predictive Analytics,” IJAIT (International Journal of Applied Information Technology), vol. 7, no. 02, p. 79, Aug. 2023, doi:10.25124/ijait.v7i02.5980.

Q. Ling, “Machine learning algorithms review,” Applied and Computational Engineering, vol. 4, no. 1, pp. 91–98, May 2023, doi:10.54254/2755-2721/4/20230355.

C. Chai, J. Wang, Y. Luo, Z. Niu, and G. Li, “Data Management for Machine Learning: A Survey,” IEEE Transactions on Knowledge and Data Engineering, pp. 1–1, 2022, doi: 10.1109/tkde.2022.3148237.

A. Bianchi et al., “Trustworthy Machine Learning Predictions to Support Clinical Research and Decisions,” 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), Jun. 2023, doi: 10.1109/cbms58004.2023.00222.

A. Nagar and J. Sharma, ‘‘A comparative evaluation of supervised learning and unsupervised learning,’’ Resmilitaris, vol. 12, no. 5, pp. 1498–1507, 2022. [Online]. Available: https://resmilitaris.net/menuscript/index.php/resmilitaris/article/view/2285.

H. Nugroho, “A Review: Data Quality Problem in Predictive Analytics,” IJAIT (International Journal of Applied Information Technology), vol. 7, no. 02, p. 79, Aug. 2023, doi:10.25124/ijait.v7i02.5980.

T. Talaei Khoei, H. Ould Slimane, and N. Kaabouch, “Deep learning: systematic review, models, challenges, and research directions,” Neural Computing and Applications, vol. 35, no. 31, pp. 23103–23124, Sep. 2023, doi: 10.1007/s00521-023-08957-4.

G. Villa, C. Tipantuña, D. S. Guamán, G. V. Arévalo, and B. Arguero, “Machine Learning Techniques in Optical Networks: A Systematic Mapping Study,” IEEE Access, vol. 11, pp. 98714–98750, 2023, doi:10.1109/access.2023.3312387.

IBM Cloud Education. ”Supervised Learning”. https://www.ibm.com/topics/supervised-learning, 19 August 2020.

B. Mahesh, “Machine Learning Algorithms - A Review,” International Journal of Science and Research (IJSR), vol. 9, no. 1, pp. 381–386, Jan. 2020, doi: 10.21275/art20203995.

IBM. What is Unsupervised Learning?. Accessed: Mar. 15, 2022. [Online]. Available: https://www.ibm.com/topics/unsupervised-learning.

SR. Jena. “Machine Learning Concepts for Beginners: Theory and Applications”. Scholars' Press, 2023.

E. J. Salazar, M. E. Samper, and H. D. Patiño, “Dynamic customer demand management: A reinforcement learning model based on real-time pricing and incentives,” Renewable Energy Focus, vol. 46, pp. 39–56, Sep. 2023, doi: 10.1016/j.ref.2023.05.004.

T. C. Phan, A. Pranata, J. Farragher, A. Bryant, H. T. Nguyen, and R. Chai, “Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain,” Sensors, vol. 22, no. 17, p. 6694, Sep. 2022, doi: 10.3390/s22176694.

S. Aslangul, “Detecting Tunnels for Border Security based on Fiber Optical Distributed Acoustic Sensor Data using DBSCAN,” Proceedings of the 9th International Conference on Sensor Networks, 2020, doi: 10.5220/0008869600780084.

Y. S. Aljarbou and F. Haron, “Determining the Presence of Metabolic Pathways using Machine Learning Approach,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 8, 2020, doi: 10.14569/ijacsa.2020.0110845.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).