A Novel Random Forest Dissimilarity Measure for Multi-View Learning
| Autor: | Hongliu Cao, Simon Bernard, Laurent Heutte, Robert Sabourin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Measure (data warehouse) Computer Science - Machine Learning Exploit business.industry Computer science Context (language use) Machine Learning (stat.ML) 02 engineering and technology Machine learning computer.software_genre Task (project management) Random forest Machine Learning (cs.LG) Statistics - Machine Learning 020204 information systems Metric (mathematics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Dimension (data warehouse) business computer Large margin nearest neighbor |
| Zdroj: | ICPR |
| Popis: | Multi-view learning is a learning task in which data is described by several concurrent representations. Its main challenge is most often to exploit the complementarities between these representations to help solve a classification/regression task. This is a challenge that can be met nowadays if there is a large amount of data available for learning. However, this is not necessarily true for all real-world problems, where data are sometimes scarce (e.g. problems related to the medical environment). In these situations, an effective strategy is to use intermediate representations based on the dissimilarities between instances. This work presents new ways of constructing these dissimilarity representations, learning them from data with Random Forest classifiers. More precisely, two methods are proposed, which modify the Random Forest proximity measure, to adapt it to the context of High Dimension Low Sample Size (HDLSS) multi-view classification problems. The second method, based on an Instance Hardness measurement, is significantly more accurate than other state-of-the-art measurements including the original RF Proximity measurement and the Large Margin Nearest Neighbor (LMNN) metric learning measurement. accepted to ICPR 2020 (22/06/2020) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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