Timeline for Finding outliers in multiple dimensions
Current License: CC BY-SA 3.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 25, 2018 at 0:42 | comment | added | kjetil b halvorsen♦ | For your example here mahalanobis distance would work | |
| May 3, 2016 at 20:40 | comment | added | Mauricio Ramalho Custodio | If I am understainding your problem right, you don't need to model your features. Let your anomaly detection system identify all your outliers and if you wish to select only those datas with high profit and low cost you can filter them using (Xprofit > MUprofit)*(Xcost < MUprofit), where X is your data and MU is the mean. You will end up with a vector with 0s and 1s, where the ones are the outliers that satisfies all your conditions. | |
| May 2, 2016 at 5:34 | comment | added | tourist | I've tried out anomaly detection as well , but the problem is how to model the features like high profit, low cost .. I've tried to overcome it using zscore using negative sign for low cost any input for the same in anomaly detection? | |
| Apr 30, 2016 at 14:12 | history | answered | Mauricio Ramalho Custodio | CC BY-SA 3.0 |