Abstract
This paper considers the following problem in multi-agent data analysis: given a tuple of k-valued decision tables (a distributed decision table), construct an approximate shared test of specified accuracy and minimum cardinality that can be applied simultaneously to each of the tables. Because this problem is NP-hard, new theoretical results related to the accuracy of the proposed algorithm were reported, and experimental verification was conducted for the datasets from the stylometry domain. The experiments were carried out taking into account the perspectives of knowledge representation and performance of classifiers for the proposed algorithm.
Paper Type
Short Paper
DOI
10.62036/ISD.2026.85
Optimization of Approximate Tests for Distributed Tables
This paper considers the following problem in multi-agent data analysis: given a tuple of k-valued decision tables (a distributed decision table), construct an approximate shared test of specified accuracy and minimum cardinality that can be applied simultaneously to each of the tables. Because this problem is NP-hard, new theoretical results related to the accuracy of the proposed algorithm were reported, and experimental verification was conducted for the datasets from the stylometry domain. The experiments were carried out taking into account the perspectives of knowledge representation and performance of classifiers for the proposed algorithm.
Recommended Citation
Ostonov, A., Zielosko, B., Jabloński, K., Stańczyk, U. & Moshkov, M.(2026). Optimization of Approximate Tests for Distributed Tables. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.85