Trilha Machine Learning - Mineração de dados em séries ...€¦ · Sistemas Biométricos...
Transcript of Trilha Machine Learning - Mineração de dados em séries ...€¦ · Sistemas Biométricos...
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Trilha Machine Learning - Mineração de dados em séries temporais para sistemas biométricos
Henrique Passos
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Mestre em Sistemas de Informação (EACH-USP)Lead consult (Systems Architect, Machine Learning Engineer)Automobilismo (Kart, Virtual)https://www.linkedin.com/in/henrique-s-passos/
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Biometria
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Biometria
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Biometria
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Biometria
P
Q
R
S
T
U
An ecg classifier designed using modified decision based neural networks 1
1[Simon and Eswaran, 1997]
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Biometria
P
Q
R
S
T
U
QRS
P
T
QS
PS
QT
PQ ST
PT
Ecg analysis: a new approach in human identification 2
2[Biel et al., 2001]
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Biometria
Sistema biométrico baseado em sinais de ECG:Pré-processamento de dados brutosExtração de características
Características fiduciaisCaracterísticas não fiduciaisCaracterísticas hibridas
ClassificaçãoIdentificação
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Biometria
Sistema biométrico baseado em sinais de ECG:Pré-processamento de dados brutos (Pan Tompkins)Extração de características
Características fiduciaisCaracterísticas não fiduciaisCaracterísticas hibridas
ClassificaçãoIdentificação
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Biometria
Sistema biométrico baseado em sinais de ECG:Pré-processamento de dados brutos (Pan Tompkins)Extração de características
Características fiduciaisCaracterísticas não fiduciaisCaracterísticas hibridas
Classificação (KNN - distância euclidiana)Identificação
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Biometria
Sistema biométrico baseado em sinais de ECG:Pré-processamento de dados brutos (Pan Tompkins)Extração de características
Características fiduciaisCaracterísticas não fiduciaisCaracterísticas hibridas
Classificação (KNN - distância euclidiana)Identificação (FAR, FRR)
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Biometria
Sistema biométrico baseado em sinais de ECG:Pré-processamento de dados brutos (Pan Tompkins)Extração de características ←
Características fiduciaisCaracterísticas não fiduciaisCaracterísticas hibridas
Classificação (KNN - distância euclidiana)Identificação (FAR, FRR)
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Biometria
Sistema biométrico baseado em sinais de ECG:Pré-processamento de dados brutos (Pan Tompkins)Extração de características ←
Características fiduciaisCaracterísticas não fiduciais ←Características hibridas
Classificação (KNN - distância euclidiana)Identificação (FAR, FRR)
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Representações de séries temporais
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Representações de séries temporais
Time Series Representations
Model Based Data Adaptive Non Data Adaptive Data Dictated
Grid Clipped
Data
Piecewise
Aggregate
Approximation
Chebyshev
Polynomials
Piecewise
Linear
Approximation
Slope
Based
Value
Based
Bi-OrthonormalOrthonormal
Wavelets SpectralRandom
MappingsSingular
Value
Approximation
Discrete
Cosine
Transform
Discrete
Fourier
Transform
SymletsCoi�etsHaar Daubechies
dbn n > 1
Sorted
Coe�cients
Adaptive
Piecewise
Constant
Approximation
Interpolation Regression
Natural
Language
Trees
Symbolic
Aggregate
Approximation
Non
Lower
Bounding
Hidden
Markov
Models
Statistical
Models
Strings
SymbolicPiecewise
Polynomial
Fonte: [Lin et al., 2007]
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Representações de séries temporaisA review on time series data mining 3
0 10 20 30 40 50
Tempo
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
Am
plit
ude
Mineração de dados em séries temporais3[Fu, 2011]
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Representações de séries temporaisAproximação Agregada por Parte (PAA) 4
0 10 20 30 40 50
Tempo
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
Am
plit
ud
e
Parâmetros: tamanho do segmento = 54[Keogh et al., 2001]
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Representações de séries temporaisAproximação Agregada Simbólica (SAX) 5
0 10 20 30 40 50
Tempo
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Am
plit
ude
Parâmetros: tamanho do segmento = 5, alfabeto = 45[Lin et al., 2003]
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Representações de séries temporaisAproximação Agregada Simbólica (SAX) 5
0 10 20 30 40 50
Tempo
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Am
plit
ude
Parâmetros: tamanho do segmento = 5, alfabeto = 45[Lin et al., 2003]
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Representações de séries temporaisAproximação Agregada Simbólica (SAX) 5
0 10 20 30 40 50
Tempo
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Am
plit
ude
Parâmetros: tamanho do segmento = 5, alfabeto = 45[Lin et al., 2003]
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Representações de séries temporaisAproximação Agregada Simbólica (SAX) 5
0 10 20 30 40 50
Tempo
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Am
plit
ude
aa
b
c
d
d
d
cc c
Parâmetros: tamanho do segmento = 5, alfabeto = 45[Lin et al., 2003]
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Representações de séries temporaisAproximação Agregada Simbólica Adaptativa (ASAX) 6
0 10 20 30 40 50
Tempo
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
Am
plit
ud
e
Centroides
aa
b
b
c
d
c
cc c
Parâmetros: tamanho do segmento = 5, alfabeto = 46[Pham et al., 2010]
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Representações de séries temporaisAproximação Agregada Simbólica baseado em GA (GASAX) 7
0 10 20 30 40 50
Tempo
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Am
plit
ud
e
aa
b
b
c
d
d
cc c
Parâmetros: tamanho do segmento = 5, alfabeto = 47[Fuad, 2012]
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Representações de séries temporais1D-Aproximação Agregada Simbólica (1DSAX) 8
0 10 20 30 40 50
Tempo
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
Am
plit
ud
e
Parâmetros: tamanho do segmento = 5, alfabeto = 48[Simon et al., 2013]
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Representações de séries temporaisAproximação Agregada Simbólica baseado em Variância (VWSAX) 9
0 10 20 30 40 50
Tempo
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Am
plit
ude
aa
b
ST EN
Parâmetros: tamanho do segmento = 5, alfabeto = 4, limiar = 1.29[Sun et al., 2012]
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Sistemas Biométricos
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Sistemas Biométricos
Symbolic representations of time series applied to
biometric recognition based on ECG signals
Henrique dos Santos Passos∗, Felipe Gustavo Silva Teodoro†, Bruno Matarazzo Duru‡,
Edenilton Lima de Oliveira§, Sarajane M. Peres¶ and Clodoaldo A. M. Limak
Information Systems Graduate Program
School of Arts, Science and Humanities
University of São Paulo
São Paulo, SP, Brazil
Email: ∗[email protected], †[email protected], ‡[email protected],§[email protected], ¶[email protected], [email protected]
Abstract—One reason for researching new biometr ic modalitiesis to improve the capabilities of secur ity systems against threats.Biometr ic modalities based on biomedical signals, in par ticularthe electrocardiogram signal (ECG), have been widely adopted.These can be represented by time ser ies. However, in this
is intentionally removed to prevent a person from being
identif ed. With the increased use of Biometric Systems, these
kinds of attacks are becoming more frequent and some serious
questions are beginning to be raised about this technology.
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Sistemas Biométricos
a) b) c)
d)
e)
SAX
ESAX
SAXTD
CCCCDDCEFC
CCCCDDCEFC
CCCCDDCEFC
f) g)
h)
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Sistemas Biométricos
a) b) c)
d)
e)
SAX
ESAX
SAXTD
CCCCDDCEFC
CCCCDDCEFC
CCCCDDCEFC
f) g)
h)
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Sistemas Biométricos
a) b) c)
d)
e)
SAX
ESAX
SAXTD
CCCCDDCEFC
CCCCDDCEFC
CCCCDDCEFC
f) g)
h)
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Resultados
Resultado obtido pelo Ensemble com BCPSO usando a base de dados PTBResultados do Ensemble por otimização global
Repr. Tx. treino Tx. teste EER Wilcoxon ParâmetroEFD 99.1687 99.0839 69.1219 V - 0.0002 λ = 110;µ = 4;α = 29; ν = 4;κ = 2EFVD 98.0843 97.6312 77.9495 V - 0.0002 λ = 103;µ = 1;α = 62; ν = 8;κ = 2ESAX 96.3916 92.8657 87.3971 V - 0.0002 λ = 50;µ = 4;α = 46; ν = 9;κ = 21EWD 99.8753 99.7502 93.8373 F - 0.2247 λ = 114;µ = 4;α = 36; ν = 1;κ = 1SAXTD 99.0176 99.3060 88.7203 V - 0.0003 λ = 125;µ = 4;α = 47; ν = 2;κ = 4Ensemble 99.8866 99.8334 96.1969 - Representações acima
Resultados do Ensemble por otimização individualRepr. Tx. treino Tx. teste EER Wilcoxon ParâmetroEFD 99.7355 99.5558 44.0178 V - 0.0004 λ = 104;µ = 4;α = 64; ν = 5;κ = 2EFVD 99.7280 99.7779 79.0321 V - 0.0237 λ = 122;µ = 4;α = 54; ν = 5;κ = 2ESAX 99.5126 99.6761 82.4558 V - 0.0006 λ = 123;µ = 4;α = 62; ν = 5;κ = 1EWD 99.9622 99.9260 95.4659 F - 1.0000 λ = 125;µ = 4;α = 4; ν = 1;κ = 1SAXTD 99.5126 99.6854 85.0097 V - 0.0020 λ = 116;µ = 4;α = 57; ν = 1;κ = 1Ensemble 99.9358 99.9260 95.7065 - Representações acima
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Conclusões
Identificação (classificação)FAR/FRR (biométrica)Ensemble x RepresentaçõesAplicações
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Referências
Biel, L., Pettersson, O., Philipson, L., and Wide, P. (2001).Ecg analysis: a new approach in human identification.Instrumentation and Measurement, IEEE Transactions on, 50(3):808–812.
Fu, T.-c. (2011).A review on time series data mining.Engineering Applications of Artificial Intelligence, 24(1):164–181.
Fuad, M. M. M. (2012).Genetic algorithms-based symbolic aggregate approximation.Springer.
Keogh, E., Chakrabarti, K., Pazzani, M., and Mehrotra, S. (2001).Dimensionality reduction for fast similarity search in large time series databases.Knowledge and information Systems, 3(3):263–286.
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Referências
Lin, J., Keogh, E., Lonardi, S., and Chiu, B. (2003).A symbolic representation of time series, with implications for streaming algorithms.In Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining andknowledge discovery, pages 2–11. ACM.
Lin, J., Keogh, E., Wei, L., and Lonardi, S. (2007).Experiencing sax: a novel symbolic representation of time series.Data Mining and knowledge discovery, 15(2):107–144.
Pham, N. D., Le, Q. L., and Dang, T. K. (2010).Two novel adaptive symbolic representations for similarity search in time series databases.In Web Conference (APWEB), 2010 12th International Asia-Pacific, pages 181–187. IEEE.
Simon, B. P. and Eswaran, C. (1997).An ecg classifier designed using modified decision based neural networks.Computers and Biomedical Research, 30(4):257–272.
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Referências
Simon, M. et al. (2013).1d-sax; a novel symbolic representation for time series.IDA’13.
Sun, C., Stirling, D., Ritz, C., and Sammut, C. (2012).Variance-wise segmentation for a temporal-adaptive sax.In Proceedings of the Tenth Australasian Data Mining Conference-Volume 134, pages71–77. Australian Computer Society, Inc.