Multicriteria Design of Cost-Conscious Fuzzy Rule-Based Classifiers
| Autor: | José Ranilla, Roberto Gil-Pita, Alberto Cocaña-Fernández, Luciano Sánchez |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Fuzzy rule Computer science business.industry 02 engineering and technology Fuzzy control system Machine learning computer.software_genre Multi-objective optimization Multi stage classification Power (physics) Set (abstract data type) 020901 industrial engineering & automation Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Software Information Systems |
| Zdroj: | International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 25:141-159 |
| ISSN: | 1793-6411 0218-4885 |
| DOI: | 10.1142/s0218488517400074 |
| Popis: | Many real-world classification systems must comply with a series of inherent restrictions to the problem at hand such as response times, power consumptions or computational costs. This poses a fundamental limitation to traditional performance-driven classifiers and learning algorithms by restraining their applicability in cost-sensitive scenarios. Because of this, fuzzy systems are leveraged to learn cost-conscious multi-stage classifiers through multiobjective optimization to find a set of optimal tradeoffs between accuracy and any related cost. This approach allows find a suitable balance between all objectives regardless of the scenario. Experimental evaluations were done for Sound Environment Classification in modern battery-powered hearing aids by jointly optimising classification accuracy and computational costs. |
| Databáze: | OpenAIRE |
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