Distinguishing the unexplainable from the merely unusual

Senator, Ted E.; Goldberg, Henry G.; Memory, Alex

doi:10.1145/2500853.2500861

Cited by 5 publications

(7 citation statements)

References 4 publications

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“…The presence of a cause-and-effect relationship in the identified outlier events rendered them events of interest, which is a desirable characteristic for determining whether they require further investigation for decision-making model development. Conversely, when outliers lack this cause-andeffect relationship, they may be removed from the analysis database, and further analysis may be disregarded (Senator, Goldberg & Memory, 2013).…”

Section: Detección De Comportamientos Atípicos De Contribuyentes Con ...mentioning

confidence: 99%

“…Outlier analysis allows for the interpretation of results (Aggarwal, 2017), and a relationship between the condition and effect pair can be identified in outlier data. Furthermore, when the condition-effect is explained, outlier data can be transformed into events of interest (Senator, Goldberg, & Memory, 2013).…”

Section: Outlier Detectionmentioning

confidence: 99%

“…The aim of outlier analysis is to convert atypical events into events of interest. This conversion helps to identify causeeffect relationships in anomalous data and to detect behavior patterns that are relevant to the application domain (Senator, Goldberg, & Memory, 2013). This process can be divided into three steps: a) Identify outliers using techniques that suit the domain problem (Mandhare & Idate, 2017;Aggarwal, 2017;Souiden, Omri, & Brahmi, 2022;Domingues, Filippone, & Michiar, 2018).…”

Section: Outlier Analysismentioning

confidence: 99%

“…This process can be divided into three steps: a) Identify outliers using techniques that suit the domain problem (Mandhare & Idate, 2017;Aggarwal, 2017;Souiden, Omri, & Brahmi, 2022;Domingues, Filippone, & Michiar, 2018). b) Detect events of interest in outliers using methods that explain the findings (Senator, Goldberg, & Memory, 2013;Mokoena, Celik, & Marivate , 2022;Herskind Sejr & Schneider-Kamp, 2021). c) Filter or remove outliers without patterns that are relevant to the problem domain.…”

Section: Outlier Analysismentioning

confidence: 99%

“…c) Filter or remove outliers without patterns that are relevant to the problem domain. Additionally, there are algorithms sensitive to noise and outliers (Senator, Goldberg, & Memory, 2013;Yang, Revista Politécnica, Agosto -Octubre 2023, Vol. 52, No.…”

Section: Outlier Analysismentioning

confidence: 99%

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Detecting Atypical Behaviors of Taxpayers with Risk of Non-Payment in Tax Administration, A Data Mining Framework

Ordoñez

Hallo

2023

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One of the primary processes in tax administration is debt collection management. The objective of this process, among others, is to recover economic resources that have been declared by taxpayers. Due to limitations in tax administration such as staffing, tools, time, and others, tax administrations seek to recover debts in the early stages of control where collection costs are lower than in subsequent stages. To optimize the debt collection management process and contribute to decision-making, this study proposes a deep learning-based framework to detect atypical behaviors of taxpayers with a high probability of non-payment. Normal and atypical behavior groups were also analyzed to identify interesting events using association rules.

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Section: Detección De Comportamientos Atípicos De Contribuyentes Con ...mentioning

confidence: 99%

Section: Outlier Detectionmentioning

confidence: 99%