Authors:
Vagner Seibert
1
;
Rafael Bastos
1
;
Giovani Maia
1
;
Giancarlo Lucca
2
;
Helida Santos
3
;
Adenauer Yamin
1
and
Renata Reiser
1
Affiliations:
1
Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Pelotas, Brazil
;
2
Mestrado em Engenharia Eletrônica e Computação, Universidade Católica de Pelotas, Pelotas, Brazil
;
3
Centro de Ciências Computacionais, Universidade Federal do Rio Grande, Rio Grande, Brazil
Keyword(s):
Fuzzy Logic, Air Quality, Sensor Validation, Classification Problem, Machine Learning.
Abstract:
This work considers different fuzzy classifier models to evaluate the air quality of indoor spaces, providing flexible systems related to the imprecision of metrics and parameters since the modeling process. Air Quality is a relevant topic concerning modern society, and the research on air quality evaluation provides important alternatives for improving global environmental governance. In this paper, we discuss the performances of the five fuzzy classifiers named CHI, FURIA, WF-C, FARC-HD, and SLAVE, applied in the data classification from an open dataset from Germany. Thus, this domain knowledge enables us to model the inherent uncertainties of attributes’ problems related to Air Quality and Air Quality Index. The results showed that fuzzy approaches offer a valid alternative for determining and correctly classifying indoor air quality with satisfying accuracy, adding flexible modeling in the air quality analysis.