Rule-Based Classification for Evidential Data
| Autor: | Mohamed Anis Bach Tobji, Nassim Bahri, Boutheina Ben Yaghlane |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Matching (statistics)
Association rule learning Computer science media_common.quotation_subject Rule-based system 02 engineering and technology computer.software_genre Term (time) 020204 information systems Associative classifier Dempster–Shafer theory 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Pruning (decision trees) Data mining computer media_common |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030584481 SUM |
| DOI: | 10.1007/978-3-030-58449-8_17 |
| Popis: | In this paper, we tackle the problem of multi-rules based classification for evidential data, i.e., data where imperfection is modeled through the Evidence theory. In this setting, a new algorithm called EviRC is introduced. This method uses different pruning techniques to omit irrelevant rules and defines a new matching criteria between the rules and the instance to classify. The selected rules are then combined using the powerful combination rules of the Evidence theory. Extensive experiments were conducted on several data sets in order to evaluate the proposed method. The experiments produce interesting results in term of classification quality. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|