The Development of Symbolic Expressions for Fire Detection with Symbolic Classifier Using Sensor Fusion Data
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- @Article{Andelic:2022:Sensors,
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author = "Nikola Andelic and Sandi {Baressi Segota} and
Ivan Lorencin and Zlatan Car",
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title = "The Development of Symbolic Expressions for Fire
Detection with Symbolic Classifier Using Sensor Fusion
Data",
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journal = "Sensors",
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year = "2022",
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volume = "23",
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number = "1",
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pages = "Article no 169",
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month = dec,
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email = "nandelic@riteh.hr",
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keywords = "genetic algorithms, genetic programming, symbolic
classifier, fire-alarm, oversampling methods,
undersampling methods",
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publisher = "MDPI",
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ISSN = "1424-8220",
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URL = "https://www.mdpi.com/1424-8220/23/1/169",
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DOI = "doi:10.3390/s23010169",
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size = "27 pages",
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abstract = "Fire is usually detected with fire detection systems
that are used to sense one or more products resulting
from the fire such as smoke, heat, infrared,
ultraviolet light radiation, or gas. Smoke detectors
are mostly used in residential areas while fire alarm
systems (heat, smoke, flame, and fire gas detectors)
are used in commercial, industrial and municipal areas.
However, in addition to smoke, heat, infrared,
ultraviolet light radiation, or gas, other parameters
could indicate a fire, such as air temperature, air
pressure, and humidity, among others. Collecting these
parameters requires the development of a sensor fusion
system. However, with such a system, it is necessary to
develop a simple system based on artificial
intelligence (AI) that will be able to detect fire with
high accuracy using the information collected from the
sensor fusion system. The novelty of this paper is to
show the procedure of how a simple AI system can be
created in form of symbolic expression obtained with a
genetic programming symbolic classifier (GPSC)
algorithm and can be used as an additional tool to
detect fire with high classification accuracy. Since
the investigation is based on an initially imbalanced
and publicly available dataset (high number of samples
classified as 1-Fire Alarm and small number of samples
0-No Fire Alarm), the idea is to implement various
balancing methods such as random
undersampling/oversampling, Near Miss-1, ADASYN, SMOTE,
and Borderline SMOTE. The obtained balanced datasets
were used in GPSC with random hyperparameter search
combined with 5-fold cross-validation to obtain
symbolic expressions that could detect fire with high
classification accuracy. For this investigation, the
random hyper-parameter search method and 5-fold
cross-validation had to be developed. Each obtained
symbolic expression was evaluated on train and test
datasets to obtain mean and standard deviation values
of accuracy (ACC ), area under the receiver operating
characteristic curve (AUC , respectively. The symbolic
expression using which best values of classification
metrics were achieved is shown, and the final
evaluation was performed on the original dataset.",
- }
Genetic Programming entries for
Nikola Andelic
Sandi Baressi Segota
Ivan Lorencin
Zlatan Car
Citations