Difference between revisions of "...find outliers"

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* [[AI Solver]]
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Anomaly detection. Sometimes the goal is to identify data points that are simply unusual. In fraud detection, for example, any highly unusual credit card spending patterns are suspect. The possible variations are so numerous and the training examples so few, that it's not feasible to learn what fraudulent activity looks like. The approach that anomaly detection takes is to simply learn what normal activity looks like (using a history non-fraudulent transactions) and identify anything that is significantly different.
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Do you have > 100 features?
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[https://www.youtube.com/results?search_query=~outlier+~diversity+AI YouTube]
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[https://www.quora.com/search?q=~outlier%20diversity%20AI ... Quora]
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[https://www.google.com/search?q=~outlier+~diversity+AI ...Google search]
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[https://news.google.com/search?q=~outlier+~diversity+AI ...Google News]
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[https://www.bing.com/news/search?q=~outlier+~diversity+AI&qft=interval%3d%228%22 ...Bing News]
  
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* Yes, then try [[One-class Support Vector Machine (SVM)]]
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* [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
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* No, need fast training, then try [[Principal Components Analysis (PCA)-based Anomaly Detection]]
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* [[AI Solver]]
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** Looking for event based?
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*** [[Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism]]
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** Do you have > 100 features?
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*** Yes, then try [[One-class Support Vector Machine (SVM)]]
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*** No, need fast training, then try [[Principal Component Analysis (PCA)]]-based Anomaly Detection
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** Also consider...
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*** [[K-Means]] Clustering
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*** [[Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)]]
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*** [[One-class Support Vector Machine (SVM)]]
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**** [[Variational Autoencoder (VAE)]]
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*** [[Autoencoder (AE) / Encoder-Decoder]]
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*** [[Radial Basis Function Network (RBFN)]]
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* [https://en.wikipedia.org/wiki/Outlier Outlier | Wikipedia]
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* [https://medium.com/@srnghn/machine-learning-trying-to-detect-outliers-or-unusual-behavior-2d9f364334f9 Machine Learning: Trying to detect outliers or unusual behavior | Stacey Ronaghan - Medium]
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* [https://pdfs.semanticscholar.org/4c68/4a9ba057fb7e61733ff554fe2975a2c91096.pdf Outlier Detection and Removal Algorithm in K-Means and Hierarchical Clustering | A. Barai and L. Dey]
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* [[Cybersecurity]] ... [[Open-Source Intelligence - OSINT |OSINT]] ... [[Cybersecurity Frameworks, Architectures & Roadmaps | Frameworks]] ... [[Cybersecurity References|References]] ... [[Offense - Adversarial Threats/Attacks| Offense]] ... [[National Institute of Standards and Technology (NIST)|NIST]] ... [[U.S. Department of Homeland Security (DHS)| DHS]] ... [[Screening; Passenger, Luggage, & Cargo|Screening]] ... [[Law Enforcement]] ... [[Government Services|Government]] ... [[Defense]] ... [[Joint Capabilities Integration and Development System (JCIDS)#Cybersecurity & Acquisition Lifecycle Integration| Lifecycle Integration]] ... [[Cybersecurity Companies/Products|Products]] ... [[Cybersecurity: Evaluating & Selling|Evaluating]]
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* [[Signal Processing]]
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* [[Pathology]]
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<hr><center>
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<b>Outliers don't necessarily represent abnormal behavior
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An Outlier is a rare chance of occurrence within a given data set. In Data Science, an Outlier is an observation point that is distant from other observations. An Outlier may be due to variability in the measurement or it may indicate experimental error. Outliers, being the most extreme observations, may include the sample maximum or sample minimum, or both, depending on whether they are extremely high or low. However, the sample maximum and minimum are not always outliers because they may not be unusually far from other observations. - [https://medium.com/@mehulved1503/outlier-detection-and-anomaly-detection-with-machine-learning-caa96b34b7f6 Outlier Detection and Anomaly Detection with Machine Learning | Mehul Ved - Medium]
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<youtube>MUcrGtLKK7I</youtube>
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<youtube>QaVL4Ht3u8w</youtube>

Latest revision as of 04:36, 13 September 2023

YouTube ... Quora ...Google search ...Google News ...Bing News


Outliers don't necessarily represent abnormal behavior


An Outlier is a rare chance of occurrence within a given data set. In Data Science, an Outlier is an observation point that is distant from other observations. An Outlier may be due to variability in the measurement or it may indicate experimental error. Outliers, being the most extreme observations, may include the sample maximum or sample minimum, or both, depending on whether they are extremely high or low. However, the sample maximum and minimum are not always outliers because they may not be unusually far from other observations. - Outlier Detection and Anomaly Detection with Machine Learning | Mehul Ved - Medium


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