reading group

1. Topic:
Machine learning Synthetic minority over-
Imbalanced data sets sampling technique (SMOTE)
Presented by Hector Franco
TCD

2. Basic concepts

Introduction
1.
Recent developments
2.
Algorithms description.
3.
Evaluation.
4.
Discursion.
5.

3. 0

4. Multi class problems are imbalance when we

compare one against all.
In some cases the data set is very small, to

generalize well.
Text classification is an example of imbalanced

data.
It can be use with tree-kernels.


5. Effect of SMOTE and DEC – (SDC)
After DEC alone After SMOTE
and DEC

6. : Majority sample
: Minority sample
: Synthetic sample
6

8. introduction

9. By convention the class with less number of

examples is called minority or positive
samples.

10. The recent developments in
imbalanced data sets learning

11. Between-class imbalanced.

(where we focused on)
Within-class imbalanced.

It is important in text classification.

We focused on the minority class, we want a

high prediction for the minority class..
Two class problem = multiclass problem .


12. NOT VERY GOOD
IN UNBALANCED
DATA
Popular evaluation for
imbalance problem.
Usually B=1, and =1
in this paper

13. AUC:
TP rate
AREA
UNDER
ROC
FP rate

14. Data level: Change the distribution

◦ make the data balanced
Modify the existing data mining algorithms

◦ Make new algorithms

15. Random oversampling: duplicate

Random under sampling: (can remove

important data)
Remove noise

SMOTE

Combine under sampling and over sampling.

Find the hard examples and over sample

them.

16. Adaboost (increase weights of misclassified),

it does not perform well on imbalances ds. 
Improve updated weights of TP & FP, better
than weights of prediction based on TP & FP.
Use a kernel of SVM

Use a BMPM

Biased Mini max Probability Machine.
There are other cost-based learning…


17. A new Over-Sampling Method:
Borderline-SMOTE.

18. Algorithms usually

try to learn the
borderline, as
exactly as possible.

19. Borderline-SMOTE1

Borderline-SMOTE2


22. Also oversampling the majority class.

The random numbers are between 0 and 0.5

so the synthetic examples are more close to
each other.

26. Experiments

28. Nothing: base line.

SMOTE

Random over-sampling

Borderline-SMOTE1

Borderline-SMOTE2

K=5

10 Fold cross validation.

C4.5 classified

We only want to improve the prediction of the

minority class

33. conclusion

34. Is a common problem to work with

imbalanced data sets.
Borderline examples are more easy to

misclassified.
Our methods are better than traditional

SMOTE.
Open to research:

◦ how to define DANGER examples.
◦ Determination of number of examples in DANGER.
◦ Combine to data mining algorithms.

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