User:SummerNightmare2023/Anomaly detection/CarpenterAnt Peer Review

General info

 * Whose work are you reviewing?


 * Link to draft you're reviewing
 * User:SummerNightmare2023/Anomaly detection
 * Link to the current version of the article (if it exists)
 * Anomaly detection

Evaluate the drafted changes
Lead

I think providing a definition of anomaly is a good way to organize the leading statement. However, the following sentence defining anomaly detection may need to be restructured, as the sentence implicitly defines anomaly, and therefore comes off as redundant.

Anomaly Types

This section is definitely more cleanly organized than the current 'Popular Techniques' section. However, there's a good deal of overlap between that section and your new 'Techniques' section. It's not entirely clear to me if you intend to entirely replace the 'Popular Techniques' section or interleave your contributions into it.

Deep Anomaly Detection

I think the use of deep learning in anomaly detection is absolutely worthy of its own section. It is a big topic of deep learning and almost any future developments in the field are probably going to be under the umbrella of deep learning.

The first paragraph is great, I'm just going to suggest a few rewordings:


 * "In recent years, deep learning has been widely discussed as a potential solution for dealing with the challenges facing by the anomaly detection, leading to the field of "deep anomaly detection." Using deep learning in anomaly detection can be understood as “deep anomaly detection” for short.
 * "The purpose of deep anomaly detection is to find out identify the features in the of data or anomaly points through using the use of neural networks"
 * " Much evidence shows that deep learning has profoundly better results in outperforms traditional methods in anomaly detection and demonstrates promising ability to solve problem in real world applications."

The second paragraph is a brief summary of neural network architectures, which I don't think needs to be included. I think linking to the deep learning and neural network pages in the previous paragraphs is sufficient. Instead, I suggest you fold the "Deep Anomaly Detection Categories" section into the "Deep Anomaly Detection" section. Make mention of the different ways deep learning contributes to anomaly detection (feature extraction/representation, end-to-end anomaly scoring) here. If you really do believe this model is particularly of note, perhaps it should be included in the existing Software section.

Overall, excellent work. The original article is definitely in need of an update with respect to new/deep methods in anomaly detection, and these edits will represent an important contribution to that end!