1. “To Explain or To Predict”
“To Know or To Act”
(Pure Science vs. Engineering, 2004)
Using Target-Based Bayesian Nets for Suspects
Monitoring (joint work with A. Gruber and S. Yanovski)
Irad Ben-Gal
Tel Aviv University

2. DOE: Vs-optimal designs Ginsburg & Ben-Gal (2004)
x (control) f(x) Y (output)
 f(x) known: f(x)/x=0  x*
 f(x) unknown:
 Estimate g(x) (Meta Model: DOE, RSM,…)
  g(x)/x=0  x* (R.V.)
 ‘Scientists’ (to Know): Best estimation of f(x)
 min V() (e.g., D-optimal exp.)
 ‘Practitioner’ (to act) : Best estimation of x*
 min V(x*) (new DOE optimality criterion)
Tel Aviv University
Department of Industrial Engineering

3. The Bias-Variance Tradeoff
Tel Aviv University
Department of Industrial Engineering

4. Presentation Layout
 Bayesian networks and classifiers
 Targeted Bayesian Network Learning (TBNL) (with Gruber)
 TBNL application on suspects monitoring
 Summary
Tel Aviv University
Department of Industrial Engineering
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5. Bayesian Networks (Pearl, 85)
Tel Aviv University
Department of Industrial Engineering

6. What is a Bayesian Network?
Joint Probability
B ( G , Θ ) encodes the domain’s JPD Distribution
X1 X2 X3 X4 Prob.
1 1 1 2 0.083
G  V , E  = Directed Acyclic Graph 1 1 2 2 0.167
1 2 2 3 0.25
2 2 1 1 0.25
2 2 2 1 0.25
Θ(X 3)
X2 1 2
1 0.33 0.33
2 0.67 0.67
A Complete
Factorization Bayesian Network
P (X )  P ( X 2 )P ( X 3 | X 2 )P ( X 4
| X 3, X 2 )P( X 1 | X 4, X 3, X 2 )
Tel Aviv University
Department of Industrial Engineering
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7. Explain or Predict (classify)
Chow & Liu (1968) TBNL
Tree / GBN
Williamson (2000) Gruber & Ben-Gal (2010)
p(X ) p(X )
True distribution
q(X ) q(X )
Modeled distribution
p(X ) pX   p  X i | x ' p x '
Objective i
x ' X X i
Principle Minimize D KL  p  X  || q  X   Minimize D KL  p  X i
 || q  X i  
Maximize I X i; Z i 
Maximize  I  X i
;Zi 
Consequence Maximize  I  X 
i
j
;Z j
X jZ i
Tel Aviv University
Department of Industrial Engineering
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8. Unconstrained Learning
Assume X is the target variable
3
GBN (adding-arrows) Target-Oriented (TBNL)
i=1 i=4 i=3 i=4 i=1
Equivalent Encoding!!!
Tel Aviv University
Department of Industrial Engineering
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9. Constrained Learning
Assume X is the target variable
3
GBN (adding-arrows) Target-Oriented (TBNL)
i=1 i=4 i=3 i=4 i=1
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Department of Industrial Engineering
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10. Differential Complexity
Explain
Predict (Classify)

r

t
𝜂 𝑡 = maximum percentage relative information exploitation about the target
𝜂 𝑟 = maximum percentage relative information exploitation about the rest attributes
Tel Aviv University
Department of Industrial Engineering

11. Results (1/2)
Data Sets Properties and Testing Methods
Dataset # Attributes # Classes # Instances Test Instances/Attributes Ratio
australian 14 2 690 CV5 ~49
breast 9 2 683 CV5 ~76
chess 36 2 3196 holdout ~89
cleve 11 2 196 CV5 ~18
corral 6 2 128 CV5 ~21
crx 15 2 653 CV5 ~44
german 20 2 1000 CV5 ~50
glass 9 7 214 CV5 ~24
Iris 5 3 150 CV5 ~30
lymphography 18 4 148 CV5 ~8
mofn-3-7-10 10 2 1324 holdout ~132
vote 16 3 435 CV5 ~27
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Department of Industrial Engineering
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12. Naïve Bayes: Predict
Corral Dataset
Class
A0
B0 Correlated
Irrelevant
A1 B1
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Department of Industrial Engineering
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13. Tree Augmented Network (TAN)
Class
Class Class
Correlated
Irrelevant
B0
Irrelevant
A0 Correlated
A1 A0
A0
B0
B1 Irrelevant B0
A1
A1
Correlated
B1
B1
Class Class
Class
B1
A0
A1
A1
B0 B1
Irrelevant B0
B0
A0
A0
Correlated A1
Irrelevant
Irrelevant
B1
Correlated
Correlated
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Department of Industrial Engineering
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14. Managing the Trade-off
CV5
CV5
Holdout
2/3:1/3
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Department of Industrial Engineering
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15. Results (2/2)
Accuracy
Dataset TBNL BNC-2P NB TAN C4.5 HGC
australian 83.3 87.0 85.1 82.5 84.9 85.6
breast 95.9 95.8 97.6 96.5 93.9 97.6
chess 96.9 95.8 87.3 92.4 99.5 95.3
cleve 81.4 80.0 82.1 78.4 79.4 78.7
corral 100.0 98.8 87.2 98.6 98.5 100.0
crx 86.4 84.2 85.0 83.7 86.1 86.9
german 69.7 73.6 75.4 73.9 72.9 72.5
glass 60.0 58.3 55.9 54.2 59.3 31.2
Iris 97.0 95.8 93.0 92.4 96.0 95.7
lymphography 81.8 83.7 83.4 82.2 78.4 63.8
mofn-3-7-10 100.0 91.4 86.7 91.5 84.0 86.7
vote 96.0 95.8 90.1 94.9 94.7 95.4
Average 87.4 86.7 84.1 85.1 85.6 82.4
StdE 4% 3% 3% 4% 3% 6%
Best & worst methods (incl. 5% runner up) in Bold & Italic respectively Paired t-tests show significance
Tel Aviv University
Department of Industrial Engineering
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16. Presentation Layout
 Bayesian networks and classifiers
 Targeted Bayesian Network Learning (TBNL)
 TBNL application on suspects monitoring (w. Gruber & Yanovski)
 Summary
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Department of Industrial Engineering
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17. Domain Description
 Motivation
 Simplicity: complexity-error tradeoff
 Information extraction: utilization of meta-data
 Support: help the expert understand
 Available Data
 CDR
 Privatized
 Laundered
 Requirements
 50% Recall with 1% False Alarm at most
Tel Aviv University
Department of Industrial Engineering
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18. Data Description of the Domain
Call Detail Record (CDR)
Field Description
Main party Monitored Object unique IDENTIFIER
Other party Other Party unique IDENTIFIER
year Year of call start
month Month of call start
day Day of call start
hour Hour of call start
minute Minute of call start
second Second of call start
duration Call duration in Seconds
caller Indication of call initiator : {1/0}
1 – main party initiated the call
0 – other party initiated the call
type_id Type of interaction initiator : {1/0}
1 - phone call
0 - sms (text message)
tag Type (group) of monitored Object : {1/0}
0 – main party is a non-target
1 – main party is a target
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Department of Industrial Engineering
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19. ROC curve
40 suspects to no avail
1900
missed
targets
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20. Feature Extraction
Activity of calls during the day of two distinct groups
Inter_prc_q1, Inter_prc_q2, Inter_prc_q3, Inter_prc_q4 – percentage of
activities in 1st, 2nd, 3rd and 4th quarter of the day
Tel Aviv University
Department of Industrial Engineering
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21. Learning & Mining Mobility Patterns
(PI’s: Ben-Gal, Toch and Lerner, 2012)

22. Conclusions
 “To Explain or to Predict” –
“To know or to Act” (constraint modeling)
 Managing the error-complexity tradeoff!
 An “engineering approach” to modeling
 Target-based BN Learning (2006), Gruber and Ben-Gal (2010)…
 Vs-optimality criterion  min V(x*), Ginsburg and Ben-Gal (2006)
 VOBN Ben-Gal et at (2005) – scenario dependent
 More….
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Department of Industrial Engineering 32/35

23. Prediction can help…
Tel Aviv University
Department of Industrial Engineering