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<p>An analysis of many attributes may be used to formulate the model selection process for anomaly detection as a multi-attribute decision-making (MADM) issue. Selecting the model that best balances these attributes is the aim to detect abnormalities in the current environment. By taking into account several variables at once, MADM approaches assist in methodically comparing and choosing the best anomaly detection model. Thus, in this article, we devise a procedure of MADM in the environment of the bipolar complex fuzzy set (BCFS), and for that, we originate various probability aggregation operators (AOs) such as probability bipolar complex fuzzy (BCF) weighted averaging (P-BCFWA), probability BCF ordered weighted averaging (P-BCFOWA), immediate probability BCF ordered weighted averaging (IP-BCFOWA), probability BCF weighted geometric (P-BCFWG), probability BCF ordered weighted geometric (P-BCFOWG), and immediate probability BCF ordered weighted geometric (IP-BCFOWG) operators. Afterward, we explore a case study “selection of optimal anomaly detection model” by considering hypothetical data and employing the originated procedure of MADM in the environment of BCFS. To highlight the advancement and potential of the originated MADM procedure and operators, we interpret a comparison of the developed theory with certain prevailing theories.</p>

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Selection of anomaly detection model by using multi-attribute decision-making technique relying on probability bipolar complex fuzzy aggregation operators

  • 期刊论文

  • 发表日期：

    2026年1月24日

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  • Ubaid ur Rehman,
  • Tahir Mahmood

摘要

An analysis of many attributes may be used to formulate the model selection process for anomaly detection as a multi-attribute decision-making (MADM) issue. Selecting the model that best balances these attributes is the aim to detect abnormalities in the current environment. By taking into account several variables at once, MADM approaches assist in methodically comparing and choosing the best anomaly detection model. Thus, in this article, we devise a procedure of MADM in the environment of the bipolar complex fuzzy set (BCFS), and for that, we originate various probability aggregation operators (AOs) such as probability bipolar complex fuzzy (BCF) weighted averaging (P-BCFWA), probability BCF ordered weighted averaging (P-BCFOWA), immediate probability BCF ordered weighted averaging (IP-BCFOWA), probability BCF weighted geometric (P-BCFWG), probability BCF ordered weighted geometric (P-BCFOWG), and immediate probability BCF ordered weighted geometric (IP-BCFOWG) operators. Afterward, we explore a case study “selection of optimal anomaly detection model” by considering hypothetical data and employing the originated procedure of MADM in the environment of BCFS. To highlight the advancement and potential of the originated MADM procedure and operators, we interpret a comparison of the developed theory with certain prevailing theories.