Relevance Matrix Generation Using Sensitivity Analysis in a Case-Based Reasoning Environment
| Autor: | Pascal Reuss, Klaus-Dieter Althoff, Wolfram Henkel, Daniel Fischer, Rotem Stram |
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| Rok vydání: | 2016 |
| Předmět: |
Process (engineering)
Computer science business.industry media_common.quotation_subject Machine learning computer.software_genre Task (project management) Matrix (mathematics) Similarity (psychology) Case-based reasoning Relevance (information retrieval) Artificial intelligence Sensitivity (control systems) Function (engineering) business computer media_common |
| Zdroj: | Case-Based Reasoning Research and Development ISBN: 9783319470955 ICCBR |
| Popis: | Relevance matrices are a way to formalize the contribution of each attribute in a classification task. Within the CBR paradigm these matrices can be used to improve the global similarity function that outputs the similarity degree of two cases, which helps facilitate retrieval. In this work a sensitivity analysis method was developed to optimize the relevance values of each attribute of a case in a CBR environment, thus allowing an improved comparison of cases. The process begins with a statistical analysis of the values in a given dataset, and continues with an incremental update of the relevance of each attribute. |
| Databáze: | OpenAIRE |
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