Addisson Salazar, Univ. Politècnica de València 1

Transcription

Contents
Instituto Telecomunicaciones
y Aplicaciones Multimedia

Procesado de señal y fusión de clasificadores: detección de fraude y otras aplicaciones

Dr. Addisson Salazar


Universitat Politècnica de València
24‐06‐2016
Contents
Background of the GTS
• Non‐Destructive Testing
• Surveillance Systems
• Biomedical Analysis
• Financial Analysis
Pattern Recognition Approach
• Statement of the problem • Available platforms: in‐Fusion, Neurodyn
• Application General Outline
Recent Themes in Signal Processing
Examples of Applications
• Credit card fraud detection
• Microarousal detection, neuropsychological tests
1
2
GTS Background
 Applications: Material quality control, Biomedical diagnosis, Bank card fraud, Surveillance, Image processing, …
g
Background of the GTS
 Research subjects: Statistical signal processing, Non‐
R
h bj t St ti ti l i l
i N
Gaussian mixtures, Non‐linear processing, Dynamic modeling, Decision fusion, Machine learning, Signal processing on Graphs
3
GTS ‐ Non Destructive Testing
 Quality control of marble rocks (US, I‐E)
4
GTS ‐ Non Destructive Testing
 Material consolidation and thickness layer detection
(US)
 Chronological classification of archaeological ceramics  Foreign body (US)
detection in food
(US)
5
Addisson Salazar, Univ. Politècnica de València
 Flaw detection and material characterization in historical walls
(US, I‐E, GPR)
6
1
GTS ‐ Surveillance Systems
 Multimodal  Apnea Audio
surveillance
?
GTS – Biomedical analysis
Visible video
diagnosis
(EEG, EMG,
EOG)
Fusion
Expert
1.5
SICAMM
1.5
ICAMM
1.5
1
1
100
150
200
250
300
350
400
50
100
150
200
250
300
350
400
50
100
150
200
250
300
350
400
2
1
sinus rhythm
x 10
 Atrial
 Intrusion detection
50
2
Infrared video
fibrillation
(ECG) 4
5
0
-5
0
0.5
1
1.5
2
time
atrial fibrillation
 Early forest fire detection
Epoch number
2
5
x 10
x 10
7
4
0
-5
0
0.5
1
1.5
2
time
x 10
7
7
8
 Cognitive structures, Epilepsy, Alzheimer
(EEG, ECoG, fMRI, DTI)
9
GTS ‐ Webmining
10
GTS ‐ Webmining
IT
Y
CT
IV
Email
Access
RA
1
4
5
Chat
Agenda
IN
TE
2
Forum
Workgroup
documents
Exercises
News
?
? ?
Achievement
3
3
1
Contents
O
RS
PE
NA
L
A
I
CT
TY
VI
+ Global
4
+ Deductive
Understanding
Organization
2
+ Sequential
+ Active
+ Inductive
Processing
+ Reflective
11
12
2
GTS ‐ Credit Card Fraud Analysis
GTS ‐ Credit Card Fraud Analysis
 Fraud detection 3
Fraud
detection
OLAP
in bank cards operations
2
?
20
10
0
0
commerce 
city



codes



identifyiers



amount 


method 
 



 

0
0.5
0.5
1
supervision
Models
1
outliers
Results
0.8
Model
estimation
0.7
True Positive Rate
R=
KL distance
30
Operation
record
d
¿Fraud?
0.6
1
0.5
0.4
0.3
0.2
0.1
0
0.02
0.04
0.06
False Positive Rate
0.08
0.1
13
Contents
14
Problem statement from Pattern Recognition
g
pp
Pattern Recognition Approach
Feature
extraction
Classification
Application
domain
?
Score
s


µ
One method solving all
M li l
Multiple methods h d
(collaborative working)
Knowledge about each category
Extreme case:
• Much and diverse information about a category
• A few information about the other category
Sources
‐ Physical models
‐ Databases
15
Architecture based on
Multiple Classifiers






16
Available platforms: in‐Fusion
filtering
General classifiers
Specialized classifiers in different feature space zones (Mixture of experts)
Multiple classifiers performing in sequence
Specialized classifiers in each of the feature vector components
Multiple classifiers performing in different space‐time
coordinates
Multiple classifiers performing in different space
time coordinates
Pool of competitive and collaborative weak classifiers (Boosting)
channel 1
..
.
time
Feature
extraction
function
Schemes of training:
• Un‐supervised, semi‐supervised
• Different historical dataset versions
• Different localization dataset versions
Pre‐
processing
cleaning
augmentation
indirect
features
splitting
dimension
reduction
ranking
Early
Fusion
Priors
Data
modeling
GMM
PDF
estimation
parametric / non‐
parametric
ICAMM
Training /
Testing ‐ 1
Late
Fusion
17
statistics
channel n
Knowledge

frequency
...
Training /
Testing ‐ n
Final
Representation
18
3
Available platforms: Neurodyn
channel 1
Application General Outline
in‐Fusion
Feature
extraction
..
.
s
t,
channel n
Pre‐
processing
SICAMM
s
t,
UGSICAMM
temporal / Spatial coding
t,
...

Training /
Testing ‐ n
s
Final
Representation
Prototype
New
developments
s
t,
Training /
Testing ‐ 1
Adaptation &
Development
Neurodyn
Early
Fusion
parametric / non‐
parametric
Priors
Late
Fusion
Objectives
• To improve the detection capabilities of the system in use
• To improve the predictive capabilities of the system in use
• To provide results from single and fused methods
• To provide several levels of spatial and temporal coding
• To accomplish the required standards
19
Contents
20
Massive scale
Time/data adaptive
Outliers, missing values
Signal processing and learning for Big Data
Challenges
g
g
Parallel, Descentralized
Models and optimization
Real‐time Real time
constraints
Robust
Cloud storage
Succint, sparse
Prediction, forecasting
Cleansing, imputation
Dimensionality reduction
Tasks
Regression, classification, clustering
21
Possible Definitions
given
L   NxT
denotes a low rank matrix
S   MxT
sparse matrix
22
Feature Life Cycle
Feature Description
D   NxM dictionary
V
NxT
Y   NxT
for modeling and measurement errors
Representation
large‐scale data set can be defined as
Data
Feedback
Y  L  DS  V
Data Collection
  1, N  x 1,T  no nulls index pairs  n, t 
P  Y   P  L  DS  V 
Feature Selection
Example
Feedback
Learning
Feature Evaluation
Network anomaly detection: Y is traffic volume over N links and T slots;
L is the nominal link‐level traffic; D is link x flow binary routing matrix;
S is parse anomalous flow
23
24
4
Algorithms and Data
Signal Processing on Graphs
Algorithms
• Decentralized and parallel algorithms
• Splitting, sequential algorithms
vn , n  1... N
Graph: set of connected nodes
A  n, m  anm
Adjacency matrix
sn , n  1... N
Signal on graph (each node is assigned certain number )
vn
• Online algorithms for streaming analytics
sn
Activation signal of brain centers
Periodic signal
Large‐scale problems‐‐‐>
Low‐complexity, real‐time algorithms capable of processing massive data sets in a parallelizable and/or fully decentralized fashion
Data
Signal on graph in semisupervised
scenario (5 of 8 nodes are of unknown class)
• Data sketching (subsampling)

Y   ar  br  cr
• Big data tensors (parallel factor analysis)
r 1
• Non‐linear modeling via kernel functions (tensor completion problem)
26
25
Multi‐Classifier Decision
Contents
p
pp
Examples of Applications
27
Fraud detection – General Outline
28
Fraud detection – Procedure Stages
x
Direct feature
extraction
Transactions
CLASIF
CLASIF
DETECTOR
11
1
CLASIF
CLASIF
DETECTOR
11
N
...
P1
PN
Dimensionality
reduction
Record
crossing
Pn= Pn[H1/x]
Labelled
transactions
1-Pn= Pn[H0/x]
Indirect feature
extraction
Confirmed
Frauds
Preprocessed
transactions
P
P= P[H1/P] =
FUSION
ƒ(P/H1)
ƒ(P/H1) + ƒ(P/H0)
P
Prototype fraud
selection
∞
∫ ƒ(P/H0)dp = PFA
>< u
Record
selection
u
(0,1)
H1
Preprocessed
transactions
H0
Fraud replicate
generation
Training
transactions
Testing
transactions
29
Training
transactions
30
5
Fraud detection – Procedure Stages
Key Performance Indicators
Testing
transactions
KPI
Entry code
selection
VDR
Training
classifier 1
Test
Classifier 1
PC 1
ADR
Classifier -1 scores
Training
classifier 2
ADT
Test
Classifier 2
PC 2
AFPR
1
Training
transactions
Definition
Value Detection Rate.
The total fraud percentage saved by the system for a certain cutoff values of score
Account Detection Rate.
The percentage of detected cards Average Detected Transaction. The mean amount of transactions required for detecting a fraudulent card
Account False to Positive Rate
Training
classifier 3
Test
Classifier 3
PC 3
AFPR=
1
True positives + False positives
False positives
=1+
True positives
True positives
Result
calculation
Fusion
Fusion
scores
Result tables
Analysis graphs
31
Example of normalized KPI tables ROC Curves for a Given Dataset Instituto Telecomunicaciones
Mean
FPR score (%) >= ADR (%) VDR (%) 0 100.00 100.00 1.0 100 49.90 1.6 ADT 4 FPR (%) ADR (%) VDR (%) 0 100.00 100.00 1.0 100 5 97.36 99.58 1.0 ADT 1
69 5 56.74 10 54.84 48.39 1.7 4 10 95.75 99.47 1.0 59 15 51.47 47.40 1.7 3 15 93.70 99.17 1.1 46 20 48.53 46.63 1.8 3 20 92.96 99.04 1.1 35 25 46.48 46.12 1.8 2 25 91.06 98.91 1.1 26 30 45.01 44.65 1.8 2 30 88.42 98.05 1.1 20 35 35
43.26 43.26
44.18 44.18
1.9 1.9
2
35 35
84.60 84.60
92.26 92.26
1.1 1.1
4
14
40 40.62 43.44 2.0 1 40 80.65 90.39 1.2 13 45 34.90 41.45 2.2 1 45 76.69 89.25 1.2 11 50 33.58 40.75 2.1 1 50 72.29 87.88 1.2 9 55 32.11 38.85 2.2 1 55 67.16 85.67 1.2 7 60 26.69 37.67 2.4 .8 60 61.58 80.90 1.3 6 65 23.02 35.03 2.4 .7 65 47.80 73.65 1.3 3 70 21.41 32.53 2.6 .6 70 31.52 39.84 2.2 1 75 20.38 31.82 2.2 .6 75 24.78 37.80 2.3 .8 80 17.74 30.58 2.3 .5 80 22.43 34.34 2.5 .6 85 15.84 27.06 2.6 .4 85 18.91 32.31 2.2 .5 90 14.08 25.50 2.5 .3 90 15.84 29.49 2.5 .4 95 10.70 22.22 2.8 .2 95 13.64 25.32 2.7 .2 0.8
0.7
0.8
True Positive Rate
score >= True Positive Rate
Minimum
32
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LDA
QDA
NGM
Fusion-MEAN
Fusion-MEDIAN
Fusion-MIN
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False Positive Rate
0.08
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Surrogate data from fraud operations Instituto Telecomunicaciones
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Surrogate joint distributions
ROC curves: real and surrogates
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0.2
0.4
-0.5
0.2
0.2
-0.05
-0.4
-0.2
0
0.2
-0.5
0.5
-0.2
0
0.5
-0.2
-0.1
0
0.1
-0.5
-0.2
-0.1
0
0.1
0
-0.2
0
-0.2
0
0.2
-0.5
0.2
-0.2
True Possitive Rate
-0.6
-0.4
-0.2
0
0.2
0.4
-0.2
0
0.2
Amount of surrogate data 0.7
0.6
05
0.5
0.4
0.3
0.2
0.2
0% 0%
50% 75% 100% Real data
Surrogates 100%
Surrogates 50%
Surrogates 75%
AUC calculated on the: Zoom in the Full ROC detection curves zone of interest 0.8708 0.0656 0 8708
0 0656
0.8641 0.0640 0.8563 0.0591 0.8678 0.0589 0.1
Comparison of Fraud operation joint distribution
0
0
0.2
0.4
0.6
False Positive Rate
0.8
1
37
True Posiitive Rate
ROC curves in the zone of interest
38
Apnea (microarousal detection)
0.7
Kind of feature
Amplitude
0.6
Spectral
0.5
Statistical
0.4
0.3
Real data
Surrogates 100%
Surrogates 50%
Surrogates 75%
0.2
Feature
Average amplitude
Maximum amplitude
Average power
Centroid frequency
Maximum frequency
Spindles ratio
TSI
ASI
Skewness
Kurtosis
Time reversibility
Third‐order self‐covariance 0.1
0
0
0.02
0.04
0.06
False Positive Rate
0.08
0.1
39
Apnea (SICAMM paremeters)
41
40
Apnea (SICAMM paremeters)
42
7
Neuropsychological Tests Instituto Telecomunicaciones
Response
Memorize
Response
Response
Audio stimuli
Memorize
Visual stimuli
Test
Fp1
AF7
AF3
F1
F3
F5
F7
FT7
FC5
FC3
FC1
C1
C3
33343536373839404142434445464748495051525354555657585960616263
EEG
signal
capture
Signal
processing
analysis
Time (s)
Time
43
44
DBN2 Figural Memory
TAVEC
DBN Subject #4
Subject #5
BNT
G-SICAMM+VI
G-SICAMM+BW
G-SICAMM
SICAMM+VI
SICAM+BW
SICAMM
ICAMM
True data
0
20
40
60
80
100 0
200
400
600
800
a) Time from the start of the test (s)
b) Time from the start of the test (s)
DBN2
DBN
BNT
G-SICAMM+VI
G-SICAMM+BW
G-SICAMM
SICAMM+VI
SICAM+BW
SICAMM
ICAMM
True data
Verbal Paired
Associates
Subject #5
0
TAVEC
Subject #6
100
200
300
400
0
c) Time from the start of the test (s)
200
400
600
800
d) Time from the start of the test (s)
46
45
Contents
JCR Journals
• Vergara L., Soriano A., Safont G., Salazar A., On the fusion of non‐independent detectors, Digital Signal Processing, vol. 50, pp. 24‐33, 2016.
• Safont G., Salazar A., Vergara L., Probabilistic Distance for Mixtures of Independent Component Analyzers, submitted to IEEE Transactions on Neural Networks and Learning Systems, 2016.
• Safont G., Salazar A., Vergara L., Gomez E., Villanueva V., Multichannel Dynamic Modeling of Non‐Gaussian Mixtures, submitted to IEEE Transactions on Neural Networks and Learning Systems, 2016.
• Igual J, Salazar A., Safont A., Vergara L., Semi‐supervised Bayesian classification of materials with impact‐echo signals, Sensors, vol. 15 no. 5, pp. 11528‐11550, 2015.
• Soriano A., Vergara L., Bouziane A., Salazar A., Fusion of Scores in a Detection Context Based on Alpha Integration, Neural Computation, vol. 27 no. 9, pp. 1983‐2010, 2015.
g
p
pp
• Safont G., Salazar A., Rodriguez A., Vergara L., New prediction methods based on Independent Component Analyzers Mixture Models, submitted to Signal Processing, 2015.
• Safont G., Salazar A., Rodriguez A., Vergara L., On Recovering Missing GPR Traces by Statistical Interpolation Methods, Remote Sensing, 6, pp. 7546‐7565, 2014.
• Rodriguez A., Salazar A., Vergara L., Analysis of split‐spectrum algorithms in an automatic detection framework, Signal Processing, vol. 92, pp. 2293–2307, 2012.
• Llinares R., Igual J., Salazar A., Camacho A., Semi‐blind source extraction of atrial activity by combining statistical and spectral features, Digital Signal Processing, vol. 21 no. 2, pp. 391‐403, 2011.
• Salazar A., Vergara L., Serrano A., Igual J., A General Procedure for Learning Mixtures of Independent Component Analyzers, Pattern Recognition, vol. 43 no. 1, pp. 69‐85, 2010.
• Salazar A., Vergara L., Miralles R., On including sequential dependence in ICA mixture models, Signal Processing, vol. 90, pp. 2314‐2318, 2010.
References
47
References
48
8
References
References
• Salazar A., Igual J., Vergara L., Agglomerative Clustering of Defects in Ultrasonic Non‐destructive Testing using Hierarchical Mixtures of Independent Component Analyzers, IEEE 2014 International Joint Conference on Neural Networks, IJCNN, pp. 2042‐2049, Beijing, China, 2014.
• Salazar A., Safont G., Vergara L., Surrogate techniques for testing fraud detection algorithms in credit card operations, 48th IEEE International Carnahan Conference on Security Technology, IEEE ICCST, pp. 1‐6, Rome, Italy, 2014.
• Safont G., Salazar A., Vergara L., Gomez E., Villanueva V., Mixtures of Independent Component Analyzers for Microarousal Detection, IEEE Second International Conference on Biomedical and Health Informatics (BHI 2014), pp. 752‐755, Valencia, Spain, 2014. • Safont G., Salazar A., Vergara L., Vidal A., Gonzalez A., Assessment of historic structures based on GPR, ultrasound, and impact‐echo data fusion, Key Engineering Materials, vol. 569‐570, pp. 1210‐1217, Dublin, 2013.
• Soriano A., Vergara L., Safont G., Salazar A., On comparing hard and soft fusion of dependent detectors, S i
S f
G S l
O
i h d d f f i
fd
d
d
Proceedings ‐ IEEE Int. Works.on Mach.Learn. for Sig. Proc., MLSP 2012, art no. 6349792, pp. 1‐6, Santander, 2012.
• Safont G., Salazar A., Vergara L., Gonzalez A., Vidal A., Mixtures of independent component analyzers for EEG prediction, Communications in Computer and Information Science, vol. 338 CCIS, pp. 328‐335, 2012.
• Salazar A., Safont G., Soriano A., Vergara L., Automatic Credit Card Fraud Detection based on Non‐linear Signal Processing, Proceedings ‐ International Carnahan Conference on Security Technology 2012, art no. 6393560, pp. 207‐212, Boston, USA, 2012.
• Safont G., Salazar A., Soriano A., Vergara L., Combination of Multiple Detectors for EEG based Biometric Identification/Authentication, Proceedings ‐ International Carnahan Conference on Security Technology 2012, art no. 6393564, pp. 230‐236, Boston, USA, 2012.
• Salazar A., Gosalbez J., Safont G., Vergara L., Data Fusion of Ultrasound and GPR Signals for Analysis of Historic Walls, Proceedings of International Simposium on Ultrasounds in the Control of Industrial Processes, UCIP 2012, IOP Conference Series: Materials Science and Engineering, Madrid, Spain, 2012.
• Salazar A., Vergara L., Llinares R., Learning Material Defect Patterns by Separating Mixtures of Independent Component Analyzers from NDT Sonic Signals, Mechanical Systems and Signal Processing, vol. 24 no. 6, pp. 1870‐
1886, 2010.
• Salazar A., Vergara L., ICA Mixtures Applied to Ultrasonic Non‐destructive Classification of Archaeological Ceramics, Journal on Advances in Signal Processing, vol. 2010, Article ID 125201, 11 pages, doi:10.1155/2010/125201, 2010.
• Vergara L., Moragues J. Gosalbez J., Salazar A., Detection of signals of unknown duration by multiple energy detectors, Signal Processing, vol. 90, pp. 719‐726, 2010.
Books and Book Chapters
• Salazar A., On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling, Springer‐Verlag, Berlin, 2013.
• Safont G., Salazar A., Rodriguez A., Vergara L., An Experimental Sensitivity Analysis of Gaussian and Non‐
Gaussian based Methods for Dynamic Modeling in EEG Signal Processing, In Encyclopedia of Information Science and Technology, Third Edition, IGI Global, pp. 4028‐404, USA, 2014.
• Salazar A., Vergara L., Perspectives on Pattern Recognition from ICA Mixture Modeling, in "Perspectives on Pattern Recognition", Nova Science Publishers, Inc., pp. 203‐223, USA, 2011.
• Salazar A., Vergara L., Knowledge Discovery from E‐Learning Activities, in "Advances in E‐Learning: Experiences and Methodologies", IGI‐Global, pp. 173‐198, USA, 2008.
International Conferences
• Salazar A., Igual J., Safont G., Vergara L., Vidal A., Image applications of agglomerative clustering using mixtures of non‐Gaussian distributions, CSCI 2015, Int. Conf. on Comp. Sci. Comp. Intell., pp. 459‐463, USA, 2015.
49
50
Thanks
asalazar@dcom.upv.es
http://www.iteam.upv.es/group/gts.html
51
9

Addisson Salazar, Univ. Politècnica de València 1

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