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Persistent homology classification algorithm.
| Content Provider | Europe PMC |
|---|---|
| Author | De Lara, Mark Lexter D. |
| Editor | Ventura, Sebastian |
| Copyright Year | 2023 |
| Abstract | Data classification is an important aspect of machine learning, as it is utilized to solve issues in a wide variety of contexts. There are numerous classifiers, but there is no single best-performing classifier for all types of data, as the no free lunch theorem implies. Topological data analysis is an emerging topic concerned with the shape of data. One of the key tools in this field for analyzing the shape or topological properties of a dataset is persistent homology, an algebraic topology-based method for estimating the topological features of a space of points that persists across several resolutions. This study proposes a supervised learning classification algorithm that makes use of persistent homology between training data classes in the form of persistence diagrams to predict the output category of new observations. Validation of the developed algorithm was performed on real-world and synthetic datasets. The performance of the proposed classification algorithm on these datasets was compared to that of the most widely used classifiers. Validation runs demonstrated that the proposed persistent homology classification algorithm performed at par if not better than the majority of classifiers considered. |
| Journal | PeerJ Computer Science |
| Volume Number | 9 |
| PubMed Central reference number | PMC10280283 |
| PubMed reference number | 37346603 |
| e-ISSN | 23765992 |
| DOI | 10.7717/peerj-cs.1195 |
| Language | English |
| Publisher | PeerJ Inc. |
| Publisher Date | 2023-01-10 |
| Publisher Place | San Diego, USA |
| Access Restriction | Open |
| Rights License | This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. © 2023 De Lara |
| Subject Keyword | Persistent homology Supervised learning Classification algorithm Topological data analysis |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science |
