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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Cannone, R. Castiello, C. Fanelli, A.M. Mencar, C. |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Informatics, University of Bari, Italy (Cannone, R.; Castiello, C.; Fanelli, A.M.; Mencar, C.) |
| Abstract | When approaching real-world problems with intelligent systems, an interaction with user is often expected. However, data-driven models are usually evaluated only in terms of accuracy, thus not involving users. In literature several works have been proposed for defining measures for interpretability assessment, however, such measures are mostly based on a structural evaluation. For this reason, we investigated a new methodology for assessing interpretability based on semantic cointension. The objective of this work is to provide empirical evidence about the usefulness of semantic cointension in facing a medical problem, namely the prediction of prognosis in Immunoglobulin A Nephropathy. An experimental session has been conducted, where fuzzy rule-based classifiers have been modeled, which are highly interpretable from the structural viewpoint. Results show that through the notion of semantic cointension it is possible to perform a semantic-driven assessment of interpretability, which also takes into account the overall fuzzy inference schema. |
| Starting Page | 1353 |
| Ending Page | 1358 |
| File Size | 201627 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781457716768 |
| ISSN | 21647151 |
| e-ISBN | 9781457716768 |
| DOI | 10.1109/ISDA.2011.6121848 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-11-22 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Support vector machines Fuzzy sets Accuracy Semantics Buildings Interpretability Semantic Cointension Fuzzy Logic Diseases Fuzzy Rule-Based Classifiers |
| Content Type | Text |
| Resource Type | Article |
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