Evolved ensemble of detectors for gross error detection
| Autor: | Helen Corbett, Allan Wilson, John McCall, Tien Thanh Nguyen, Phil Stockton, Laud Charles Ochei |
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| Rok vydání: | 2020 |
| Předmět: |
Training set
Physics::Instrumentation and Detectors Computer science Detector Particle swarm optimization Sample (statistics) 0102 computer and information sciences 02 engineering and technology Function (mathematics) 01 natural sciences 010201 computation theory & mathematics 0202 electrical engineering electronic engineering information engineering High Energy Physics::Experiment 020201 artificial intelligence & image processing Fisher's method Error detection and correction Algorithm Selection (genetic algorithm) |
| Zdroj: | GECCO Companion |
| Popis: | In this study, we evolve an ensemble of detectors to check the presence of gross systematic errors on measurement data. We use the Fisher method to combine the output of different detectors and then test the hypothesis about the presence of gross errors based on the combined value. We further develop a detector selection approach in which a subset of detectors is selected for each sample. The selection is conducted by comparing the output of each detector to its associated selection threshold. The thresholds are obtained by minimizing the 0-1 loss function on training data using the Particle Swarm Optimization method. Experiments conducted on a simulated system confirm the advantages of ensemble and evolved ensemble approach. |
| Databáze: | OpenAIRE |
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