Metrics for Inter-Dataset Similarity with Example Applications in Synthetic Data and Feature Selection Evaluation -- Extended Version
This work provides a solution for machine learning practitioners needing efficient and comprehensive inter-dataset similarity measures, though it appears incremental as it builds on existing concepts with new metrics.
The paper tackles the problem of measuring inter-dataset similarity by proposing two novel metrics, addressing limitations of existing methods like computational expense and lack of holistic perspective, and demonstrates their effectiveness through applications in synthetic data and feature selection evaluation.
Measuring inter-dataset similarity is an important task in machine learning and data mining with various use cases and applications. Existing methods for measuring inter-dataset similarity are computationally expensive, limited, or sensitive to different entities and non-trivial choices for parameters. They also lack a holistic perspective on the entire dataset. In this paper, we propose two novel metrics for measuring inter-dataset similarity. We discuss the mathematical foundation and the theoretical basis of our proposed metrics. We demonstrate the effectiveness of the proposed metrics by investigating two applications in the evaluation of synthetic data and in the evaluation of feature selection methods. The theoretical and empirical studies conducted in this paper illustrate the effectiveness of the proposed metrics.