When Features in a High Dimension Dataset Are Organized Hierarchically, There is an Inherent Opportunity to Reduce Dimensionality. Since More Specific Concepts Are Subsumed by More General Concepts, Subsumption Can Be Applied Successively to Reduce Dimensionality. We Tested Whether Sub-Sumption Could Reduce the Dimensionality of a Disease Dataset Without Impairing Classification Accuracy. We Started with a Dataset that Had 168 Neurological Patients, 14 Diagnoses, and 293 Unique Features. We Applied Subsumption Repeatedly to Create Eight Successively Smaller Datasets, Ranging from 293 Dimensions in the Largest Dataset to 11 Dimensions in the Smallest Dataset. We Tested a MLP Classifier on All Eight Datasets. Precision, Recall, Accuracy, and Validation Declined Only at the Lowest Dimensionality. Our Preliminary Results Suggest that When Features in a High Dimension Dataset Are Derived from a Hierarchical Ontology, Subsumption is a Viable Strategy to Reduce Dimensionality.Clinical Relevance - Datasets Derived from Electronic Health Records Are Often of High Dimensionality. If Features in the Dataset Are based on Concepts from a Hierarchical Ontology, Subsumption Can Reduce Dimensionality.
D. C. Wunsch and D. B. Hier, "Subsumption Reduces Dataset Dimensionality Without Decreasing Performance of a Machine Learning Classifier," Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 1618 - 1621, Institute of Electrical and Electronics Engineers, Jan 2021.
The definitive version is available at https://doi.org/10.1109/EMBC46164.2021.9629897
Electrical and Computer Engineering
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.
01 Jan 2021