Metrics of performance evaluation parameters.

<div><p>Outlier detection is essential for identifying unusual patterns or observations that significantly deviate from the normal behavior of a dataset. With the rapid growth of data science, the prevalence of anomalies and outliers has increased, which can disrupt system modeling and p...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Sunil Kumar (102321) (author)
مؤلفون آخرون:	Sudeep Varshney (21453384) (author), Usha Jain (21453387) (author), Prashant Johri (21453390) (author), Abdulaziz S. Almazyad (21453393) (author), Ali Wagdy Mohamed (21453396) (author), Mehdi Hosseinzadeh (8383239) (author), Mohammad Shokouhifar (20547564) (author)
منشور في:	2025
الموضوعات:	Biotechnology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified nearest neighbors algorithm nearest neighbor search nearest neighbor identification identifying unusual patterns gained significant attention disrupt system modeling 53 %, outperforming 07 %, recall rf feature selection learning repository datasets named eoda ), 49 %, parameter selection deep learning significantly deviate shadow features second stage results demonstrate relevant attributes rapid growth random forest parameter estimation often limited normal behavior inaccurate results highest z first stage existing techniques eoda approach enhanced knn data size data science clustering phase boruta method
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Metrics of performance evaluation parameters.

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