Proposing a Hybrid Machine Learning Technique for Efficient Outlier Detection
Proposing a Hybrid Machine Learning Technique for Efficient Outlier Detection |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-11 |
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| Year of Publication : 2024 | ||
| Author : Bondu Venkateswarlu, C. Jayaramulu |
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| DOI : 10.14445/22315381/IJETT-V72I11P120 | ||
How to Cite?
Bondu Venkateswarlu, C. Jayaramulu, "Proposing a Hybrid Machine Learning Technique for Efficient Outlier Detection," International Journal of Engineering Trends and Technology, vol. 72, no. 11, pp. 184-191, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I11P120
Abstract
Finding the best results when outliers in high-dimensional data is challenging because of data imbalance and dimensionality. Several algorithms were created in an attempt to address this problem. However, they are made using either an unsupervised learning approach or a supervised learning technique, and they can discover outliers from such data. Supervised learning methods utilize training data, whereas unsupervised learning methods provide tools for finding and applying complex patterns. The central claim of this article is that it is possible to “merge two approaches to produce a hybrid and gain from both worlds.” A state-of-the-art ML system that combines both under supervision and without methods for efficiently identifying outliers is provided to assess this claim. Furthermore, the Outlier Detection Multi-Model Approach (MMA-OD) is a method proposed in this paper. This method combines the advantages of both supervised and unsupervised learning models to improve performance. Its strength is increasing the size of the feature space. Many benchmark datasets are used to assess the suggested approach. The empirical results show that MMA-OD outperforms a wide range of alternative approaches.
Keywords
Hybrid Machine Learning, Outlier detection, Framework, ROC comparison, Execution time comparison.
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