Ikvinderpal Singh
Sapandeep Kaur Dhillon
- Assistant Professor, Department of Computer Science & Applications, Trai Shatabdi Guru Gobind Singh Khalsa College, Amritsar, Punjab, India
- Assistant Professor, Department of Computer Science, Guru Nanak Dev University, Amritsar, Punjab, India
Abstract
In the realm of machine learning, data structures play a pivotal role in facilitating efficient data manipulation, storage, and retrieval, thereby significantly impacting the performance and scalability of machine learning algorithms. In recent years, the field of machine learning has witnessed the emergence of novel data structures tailored to address scalability and efficiency challenges inherent in handling large-scale and high-dimensional data. This study provides a look at the data preprocessing, data cleaning and feature extraction in machine learning using data structures. This study explores three key emerging trends: tensor representations, sparse data structures, and graph-based neural networks, highlighting their potential to revolutionize modern machine learning applications. Furthermore, we discussed a case study of the Leveraging Emerging Data Structures in Healthcare Analytics.
Keywords: Machine learning, data structures, tensor representations, sparse data structures, graph-based neural networks
[This article belongs to International Journal of Data Structure Studies(ijdss)]
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| Volume | 02 |
| Issue | 01 |
| Received | February 16, 2024 |
| Accepted | February 19, 2024 |
| Published | February 21, 2024 |