Reasoning with unknown, not-applicable and irrelevant meta-values in concept learning and pattern discovery
| Autor: | Ryszard S. Michalski, Janusz Wojtusiak |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Learning classifier system
Computer Networks and Communications Computer science business.industry Active learning (machine learning) Algorithmic learning theory Stability (learning theory) Online machine learning Semi-supervised learning Machine learning computer.software_genre Artificial Intelligence Hardware and Architecture Unsupervised learning Artificial intelligence Instance-based learning business computer Software Information Systems |
| Zdroj: | Journal of Intelligent Information Systems. 39:141-166 |
| ISSN: | 1573-7675 0925-9902 |
| DOI: | 10.1007/s10844-011-0186-z |
| Popis: | This paper describes methods for reasoning with unknown, irrelevant, and not-applicable meta-values when learning concept descriptions from examples or discovering patterns in data. These types of meta-values represent different reasons for which regular values are not available, thus require different treatment in both rule learning and rule testing. The presented methods are handled internally, within the learning and testing algorithms, and not in preprocessing as those widely described in the literature. They have been implemented in the AQ21 multitask learning and knowledge discovery program, and experimentally tested on three real world and one designed datasets. |
| Databáze: | OpenAIRE |
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