Timeline for Does Support Vector Machine handle imbalanced Dataset?
Current License: CC BY-SA 3.0
10 events
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| S Oct 23, 2016 at 1:58 | history | edited | gung - Reinstate Monica | CC BY-SA 3.0 |
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| Oct 23, 2016 at 1:42 | review | Suggested edits | |||
| S Oct 23, 2016 at 1:58 | |||||
| Dec 2, 2014 at 2:31 | vote | accept | RockTheStar | ||
| Oct 31, 2014 at 18:52 | comment | added | RockTheStar | Cool, thanks @Dikran. Marc: yes, simple oversampling works in general. However, this depends on the situation. What happen is that you are adding "weights" to the minority data when you are oversampling the minority (replicating minority points again and again on the same locations). This essentially helps improving the "consideration" of minority example. However, the decision boundary of the classification will then become pretty tense (not general enough), that is, over-fitting may occurs). Therefore, we may have to consider some probablistic sampling techniques, like SMOTE. | |
| Oct 31, 2014 at 13:02 | comment | added | Marc Claesen | Logistic regression and SVM provide intrinsic ways. I don't know by heart for all these other methods, but oversampling the minority class works for pretty much every method (though it's not exactly mathematically elegant). | |
| Oct 31, 2014 at 12:52 | comment | added | Dikran Marsupial | logistic regression certainly does, you just weight the likelihood for positive patterns and negative patterns differently. | |
| Oct 30, 2014 at 21:24 | comment | added | RockTheStar | Cool, thanks! In addition to that, does logistic regression, navie bayes, decision tree handle such imbalance problem? | |
| Oct 30, 2014 at 19:58 | history | edited | Marc Claesen | CC BY-SA 3.0 |
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| Oct 30, 2014 at 19:51 | history | edited | Marc Claesen | CC BY-SA 3.0 |
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| Oct 30, 2014 at 19:44 | history | answered | Marc Claesen | CC BY-SA 3.0 |