Search Results for author:
Found 4 papers, 2 papers with code
Measuring the Validity of Clustering Validation Datasets
Therefore, it is essential to evaluate and compare datasets regarding their cluster-label matching (CLM), i. e., how well their class labels match actual clusters.
Creating a Cooperative AI Policymaking Platform through Open Source Collaboration
Advances in artificial intelligence (AI) present significant risks and opportunities, requiring improved governance to mitigate societal harms and promote equitable benefits.
Sanity Check for External Clustering Validation Benchmarks using Internal Validation Measures
We then propose processes to generalize internal measures to fulfill these new axioms, and use them to extend the widely used Calinski-Harabasz index for between-dataset CLM evaluation.
Contextual Outlier Interpretation
Outlier detection plays an essential role in many data-driven applications to identify isolated instances that are different from the majority.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
