Unbalanced data is a specific data configuration that appears commonly in nature. Applying machine learning techniques to this kind of data is a difficult process, usually addressed by unbalanced reduction techniques. https://www.bigdataspain.org/2017/talk/unbalanced-data-same-algorithms-different-techniques Big Data Spain 2017 November 16th - 17th Kinépolis Madrid

2. UNBALANCED
DATA: SAME
ALGORITHMS
DIFFERENT
TECHNIQUES
Eric Martin

3. UNBALANCED DATA
• Fraud
• Illness detection
• Anomalies
2
Y =
0
Y = 1

4. ALGORITHMS
POINT OF VIEW
3
▪ Accuracy
▪ 1,000,000 total TRX
▪ 10 Fraud TRX
= 99.9999%
Recall, f1score,
detection probability

5. UNDERSTANDING THE
PROBLEM
4
▪ Scattering Matrix:
Real 0
Real 1
Pron.0 Pron.1
LESS
ACCURACY
!
Trading Illness
Detection
Real 0
Real 1
Pron.0 Pron.1

6. IT DEPENDS
ON THE
PROBLEM!!
5

7. MOST COMMON PRACTISES
6
▪ Dimensionality
reduction:
▫ Smote
▫ Sintetic samples
creation
Y = 0 Y = 1 Y = 0 Y = 1

8. SAME
ALGORITHMS
DIFFERENT
TECHNIQUES
▪ If you expect different results
you have to do different
things
▪ Explote all data you have
▪ Bagging Algo: First step
Random Forest7

9. RANDOM FOREST
8
F1 F2 F3 …… … FN Y
1 1.2 25 True … 0.185 1
2 3.4 55 False… 0.211 1
3 2.2 58 True … 0.171 0
4 4.0 34 True … 0.132 1
5 1.1 63 True … 0.652 0
6 0.7 61 False… 0.153 0
7 3.3 12 False… 0.477 1
8 3.1 23 True … 0.311 1
9 1.2 29 False… 0.171 1
1
0 3.4 45 True … 0.132 0
1
1 2.1 55 True … 0.652 1
1
2 1.7 19 False… 0.189 0
1
3 3.3 12 False… 0.477 1
1
4 3.1 23 True … 0.311 1
1
5 1.2 29 False… 0.171 1
1
6 2.2 58 True … 0.171 0
1

10. RANDOM FOREST
9
F1 F2 F3 … … … FN Y
1.5 25 False … 0.185 ???
1
1
0
MAJORITY
VOTE
1

11. EM FOREST
10
F1 F2 F3 …… … FN Y
1 1.2 25 True … 0.185 1
2 3.4 55 False… 0.211 1
3 2.2 58 True … 0.171 0
4 4.0 34 True … 0.132 1
5 1.1 63 True … 0.652 0
6 0.7 61 False… 0.153 0
7 3.3 12 False… 0.477 1
8 3.1 23 True … 0.311 1
9 1.2 29 False… 0.171 1
1
0 3.4 45 True … 0.132 0
1
1 2.1 55 True … 0.652 1
1
2 1.7 19 False… 0.189 0
1
3 3.3 12 False… 0.477 1
1
4 3.1 23 True … 0.311 1
1
5 1.2 29 False… 0.171 1
1
6 2.2 58 True … 0.171 0
1

12. Tree1 Tree2 Tree3 Y
1 1 1 0 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
EM FOREST: Transforming the
problem
11
F1 F2 F3 …… … FN Y
1 1.2 25 True … 0.185 1
2 3.4 55 False… 0.211 1
3 2.2 58 True … 0.171 0
4 4.0 34 True … 0.132 1
5 1.1 63 True … 0.652 0
6 0.7 61 False… 0.153 0
7 3.3 12 False… 0.477 1
8 3.1 23 True … 0.311 1
9 1.2 29 False… 0.171 1
1
0 3.4 45 True … 0.132 0
1
1 2.1 55 True … 0.652 1
1
2 1.7 19 False… 0.189 0
1
3 3.3 12 False… 0.477 1
1
4 3.1 23 True … 0.311 1
1
5 1.2 29 False… 0.171 1
1
6 2.2 58 True … 0.171 0
1
0 1 0 1

13. EM FOREST: The new
problem
12
Tree1 Tree2 Tree3 Y
1 1 1 0 1
2 1 0 1 1
3 1 1 1 0
4 0 1 0 1
5 0 0 0 0
6 1 0 1 0
7 0 1 0 1
8 0 1 0 1
9 1 0 1 1
10 1 1 0 0
11 0 1 0 1
12 0 0 1 0
13 1 0 1 1
14 1 1 0 1
15 1 1 0 1
16 0 0 1 0
17 0 1 0 1
18 1 0 0 0

14. EM FOREST: The new
possibilities
13
Tree1 Tree2 Tree3 Y
1 1 1 0 1
2 1 0 1 1
3 1 1 1 0
4 0 1 0 1
5 0 0 0 0
6 1 0 1 0
7 0 1 0 1
8 0 1 0 1
▪ Vector vs. Aggregated
Agg Y
1 2 1
2 2 1
3 3 0
4 0 1
5 1 0
6 2 0
7 1 1
8 1 1

15. EM FOREST: The new results
14
▪ Result improvement: Better score
( at least the same ) than Random
Forest
▪ Result flexibility: Better in balanced and
unbalanced data (Trading and illness
detection )

16. EM FOREST: Adventages
15
▪ Open Source
▪ Scalability
▪ More possibilities

17. EM FOREST: Use cases
16
▪ Real projects:
Credit card usage trends
▪ Demo projects:
Bank fraud
Alcohol in students dataset

18. THANKS!
Any questions?
You can find me at:
Eric Martin
ericmartinct@gmail.com
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