The Computational Complexity of Concise Hypersphere Classification
Abstract
Hypersphere classification is a classical and foundational method that can provide easy-to-process explanations for the classification of real-valued as well as binary data. However, obtaining an (ideally concise) explanation via hypersphere classification is much more difficult when dealing with binary data as opposed to real-valued data. In this paper, we perform the first complexity-theoretic study of the hypersphere classification problem for binary data. We use the fine-grained parameterized complexity paradigm to analyze the impact of structural properties that may be present in the input data as well as potential conciseness constraints. Our results include not only stronger lower bounds but also a number of new fixed-parameter algorithms for hypersphere classification of binary data, which can find an exact and concise explanation when one exists.
Cite
Text
Eiben et al. "The Computational Complexity of Concise Hypersphere Classification." International Conference on Machine Learning, 2023.Markdown
[Eiben et al. "The Computational Complexity of Concise Hypersphere Classification." International Conference on Machine Learning, 2023.](https://mlanthology.org/icml/2023/eiben2023icml-computational/)BibTeX
@inproceedings{eiben2023icml-computational,
title = {{The Computational Complexity of Concise Hypersphere Classification}},
author = {Eiben, Eduard and Ganian, Robert and Kanj, Iyad A. and Ordyniak, Sebastian and Szeider, Stefan},
booktitle = {International Conference on Machine Learning},
year = {2023},
pages = {9060-9070},
volume = {202},
url = {https://mlanthology.org/icml/2023/eiben2023icml-computational/}
}