Symbolic Data Analysis to Improve Completeness of Model Combination Methods

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Pedro Strecht (Author) REIT; João Mendes-Moreira (Author) FEUP; Carlos Soares (Author) FEUP

Conference proceedings International

Title: AI 2023: Advances in Artificial Intelligence - 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28 - December 1, 2023, Proceedings, Part II

Pages: 107-119

36th Australasian Joint Conference on Artificial Intelligence, AJCAI 2023

Brisbane, 28 November 2023 through 1 December 2023

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Other information

Authenticus ID: P-00Z-BDR

DOI: 10.1007/978-981-99-8391-9_9

Abstract (EN): A growing number of organizations are adopting a strategy of breaking down large data analysis problems into specific sub-problems, tailoring models for each. However, handling a large number of individual models can pose challenges in understanding organization-wide phenomena. Recent studies focus on using decision trees to create a consensus model by aggregating local decision trees into sets of rules. Despite efforts, the resulting models may still be incomplete, i.e., not able to cover the entire decision space. This paper explores methodologies to tackle this issue by generating complete consensus models from incomplete rule sets, relying on rough estimates of the distribution of independent variables. Two approaches are introduced: synthetic dataset creation followed by decision tree training and a specialized algorithm for creating a decision tree from symbolic data. The feasibility of generating complete decision trees is demonstrated, along with an empirical evaluation on a number of datasets.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13