A Comparative Analysis of a Novel Custom Distance Metric Against Traditional Metrics in KNN Classification
- Ishan Shivankar,
- Bhairavi Shirsath,
- Bhakti Bhande,
- Chetak Chandewar
Bhairavi Shirsath
Vishwakarma Institute of Technology
Bhakti Bhande
Vishwakarma Institute of Technology
Chetak Chandewar
Priyadarshini College of Engineering
Abstract
This study tackles the limitations of traditional distance metrics in machine learning, particularly their inadequacy in managing missing data and varying feature importance. We introduce a novel custom distance metric that offers a more nuanced and robust measure of similarity, addressing the complexities and imperfections of real-world data. Our extensive evaluation of the CustomKNN algorithm, leveraging this custom distance metric, reveals exceptional performance and robustness across diverse datasets, consistently outperforming traditional metrics in various classification tasks. The custom distance metric shows superior robustness to missing data and scalability, despite its higher computational complexity and memory requirements. Our findings highlight the versatility and effectiveness of the CustomKNN algorithm, establishing it as a formidable option for classification tasks. This research advances the development of more adaptable and context-aware distance metrics, significantly enhancing the accuracy and reliability of machine learning models in practical applications.03 Jul 2024Submitted to Data Science and Machine Learning 05 Jul 2024Published in Data Science and Machine Learning