Timeline for Cross validation for anomaly detection using autoencoder
Current License: CC BY-SA 4.0
12 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 13, 2020 at 22:21 | answer | added | user2645976 | timeline score: 1 | |
| Oct 15, 2018 at 10:38 | vote | accept | Ashwini | ||
| Oct 1, 2018 at 4:41 | answer | added | Arpit Sisodia | timeline score: 0 | |
| Sep 25, 2018 at 17:01 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Aug 26, 2018 at 15:13 | answer | added | Jinglesting | timeline score: 0 | |
| Aug 26, 2018 at 13:03 | comment | added | redhqs | Unless I'm missing something, I don't think you're going to be able to validate until you get some feedback or labels from your domain experts! | |
| Aug 25, 2018 at 13:11 | comment | added | Ashwini | Ya that is what I am doing right now :) Thank you for your suggestion. Is there any other way to validate? | |
| Aug 25, 2018 at 10:51 | comment | added | redhqs | Sounds challenging! I think you could use an autoencoder to build a representation of your warranties, then find outliers by evaluating which points in your data are the furthest from that representation by some sort of distance measure. You could rank by furthest distance and evaluate them with your domain experts to see if the outliers are true anomalies or if you need to reconsider the features you're passing to your autoencoder. | |
| Aug 25, 2018 at 10:03 | comment | added | Ashwini | Hey! since it is warranty data, only people with business knowledge can evaluate the results. That is what I am doing right now. And I have no idea how many anomalies I will have. | |
| Aug 25, 2018 at 9:46 | comment | added | redhqs | Hi - if you don't have any labels then you'll be restricted to unsupervised methods (so cross-validation is going to be difficult). What sorts of thoughts did you have for evaluating your solution so far? Do you have an idea of how many anomalies your dataset contains and how they differ from the norm? | |
| Aug 24, 2018 at 13:35 | review | First posts | |||
| Aug 24, 2018 at 16:51 | |||||
| Aug 24, 2018 at 13:32 | history | asked | Ashwini | CC BY-SA 4.0 |