Ensemble learning for mapper parameter optimization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
60 Downloads (Pure)

Abstract

The Mapper algorithm is a technique from TDA used to create low-dimensional graph-based representations of high-dimensional data, proven effective in numerous exploratory data analysis tasks. The Mapper algorithm’s output depends on several user-chosen parameters, and selecting their values is a non-trivial choice, significantly narrowing its potential application in real-world scenarios. Research attempting to assist in selection of the parameters has been very limited to date. This paper is the first one to address the selection of Mapper’s three parameters simultaneously. The proposed idea incorporates the concept of Ensemble Learning into the Mapper algorithm. Using several datasets with known labels, we show that our method outperforms two baselines in recovering the dataset structure.

Original languageEnglish
Title of host publicationProceedings of the 35th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350342734
ISBN (Print)9798350342741
DOIs
Publication statusPublished - 20 Dec 2023
Event35th IEEE International Conference on Tools for Artificial Intelligence 2023 - Atlanta, United States
Duration: 06 Nov 202308 Nov 2023

Publication series

NameIEEE International Conference on Tools for Artificial Intelligence: proceedings
ISSN (Print)1082-3409
ISSN (Electronic)2375-0197

Conference

Conference35th IEEE International Conference on Tools for Artificial Intelligence 2023
Abbreviated titleICTAI 2023
Country/TerritoryUnited States
CityAtlanta
Period06/11/202308/11/2023

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