Energy-conscious fuzzy rule-based classifiers for battery operated embedded devices
| Autor: | José Ranilla, Luciano Sánchez, Alberto Cocaña-Fernández, Roberto Gil-Pita |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
021103 operations research
Fuzzy rule business.industry Computer science 0211 other engineering and technologies Evolutionary algorithm 02 engineering and technology Energy consumption Machine learning computer.software_genre Multi-objective optimization Knowledge base Embedded system Prior probability 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Data mining business computer Classifier (UML) |
| Zdroj: | FUZZ-IEEE |
| DOI: | 10.1109/fuzz-ieee.2017.8015483 |
| Popis: | A fuzzy rule-based classifier is proposed in this paper where the number of rules in the knowledge base that are fired when an object is classified is anti-monotone with respect to the prior probability of its class. This classifier is intended to secure an equilibrium between accuracy and energy consumption, which is critical in battery operated embedded devices. The method is compared to legacy multi-criteria evolutionary algorithms, where a group of classifiers with different balances between accuracy and consumption are evolved, and the most accurate classifier is selected among those individuals in the Pareto front whose use of the battery does not exceed a given threshold. A significant increase in the battery life is reported without a degradation in the quality of service. |
| Databáze: | OpenAIRE |
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