A Novel k-NN Approach for Data with Uncertain Attribute Values
| Autor: | Zied Elouedi, Eric Lefevre, Asma Trabelsi |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer science
business.industry media_common.quotation_subject 02 engineering and technology Machine learning computer.software_genre 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Attribute domain Artificial intelligence Belief function theory business Classifier (UML) computer Seriousness media_common |
| Zdroj: | Advances in Artificial Intelligence: From Theory to Practice ISBN: 9783319600413 IEA/AIE (1) |
| Popis: | Data uncertainty arises in several real world domains, including machine learning and pattern recognition applications. In classification problems, we could very well wind up with uncertain attribute values that are caused by sensor failures, measurements approximations or even subjective expert assessments, etc. Despite their seriousness, these kinds of data are not well covered till now. In this paper, we propose to develop a machine learning model for handling such kinds of imperfection. More precisely, we suggest to develop a new version of the well known k-nearest neighbors classifier to handle the uncertainty that occurs in the attribute values within the belief function framework. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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