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Air Quality has been discussed as a relevant and also concerning topic in our society. Indoor spaces tend to be the place were humans spend most of their time, thus the healthiness within their vicinity ought be a priority. Such goal is only achieved by robust and effective monitoring provided by reliable sensors. This paper considers several fuzzy methods to evaluate Indoor Air Quality, providing flexible systems that take into account the imprecision of metrics and parameters since the modeling process. This paper discusses five fuzzy classifiers and five other classic supervised learning methods. These algorithms are applied to an open dataset from Germany. The results indicated that the fuzzy approaches are a valid alternative for correctly classifying indoor air quality, not only with an above-average accuracy but also by adding flexible modeling capabilities to the analysis.

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Toward Air Quality Fuzzy Classification: Measuring the Performance of Fuzzy Logic Methods for Air Quality Classification

  • 会议论文

  • 发表日期：

    2026年

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  • Vagner A. Seibert,
  • Rafael Bastos,
  • Adenauer Yamin,
  • Renata H. R. Reiser,
  • Giancarlo Lucca,
  • Helida Santos

摘要

Air Quality has been discussed as a relevant and also concerning topic in our society. Indoor spaces tend to be the place were humans spend most of their time, thus the healthiness within their vicinity ought be a priority. Such goal is only achieved by robust and effective monitoring provided by reliable sensors. This paper considers several fuzzy methods to evaluate Indoor Air Quality, providing flexible systems that take into account the imprecision of metrics and parameters since the modeling process. This paper discusses five fuzzy classifiers and five other classic supervised learning methods. These algorithms are applied to an open dataset from Germany. The results indicated that the fuzzy approaches are a valid alternative for correctly classifying indoor air quality, not only with an above-average accuracy but also by adding flexible modeling capabilities to the analysis.