Adaptation knowledge discovery using positive and negative cases
| Autor: | Emmanuel Nauer, Jean Lieber |
|---|---|
| Přispěvatelé: | Nauer, Emmanuel, Data Science, Knowledge, Reasoning and Engineering (K Team), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), K team (Data Science, Knowledge, Reasoning and Engineering) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]
Exploit Computer science business.industry negative cases closed itemset extraction Filter (signal processing) Machine learning computer.software_genre Data preparation [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Knowledge extraction case-based reasoning Case-based reasoning Artificial intelligence Adaptation (computer science) business computer adaptation knowledge discovery Case base |
| Zdroj: | ICCBR 2021-29th International Conference on Case-Based Reasoning ICCBR 2021-29th International Conference on Case-Based Reasoning, Sep 2021, Salamanca (Virtual), Spain Case-Based Reasoning Research and Development ISBN: 9783030869564 ICCBR |
| Popis: | International audience; Case-based reasoning usually exploits positive source cases, each of them consisting in a problem and a correct solution to this problem. Now, the general issue of exploiting also negative cases-i.e., problem-solution pairs where the solution answers incorrectly the problem-can be raised. Indeed, such cases are "naturally" generated by a CBR system as long as it sometimes proposes incorrect solutions. This paper aims at addressing this issue for adaptation knowledge (AK) discovery: how positive and negative cases can be used for this purpose. The idea is that positive cases are used to propose adaptation rules and that negative cases are used to filter out some of these rules. In a preliminary work, this kind of AK discovery has been applied using frequent closed itemset (FCI) extraction on variations within the case base and tested on a toy Boolean use case, with promising first results. This paper resumes this study and evaluates it on 4 benchmarks, which confirms the benefit of exploiting negative cases for AK discovery. This involves some adjustments in the data preparation and in adaptation rule filtering, in particular because FCI extraction works only with Boolean features, hence some methodology lessons learned for AK discovery with positive and negative cases. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|