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Smart Tech: Biosignals & Medical Electronics
Biodata analysis
Oresti Banos
June 2nd, 2017
o.banoslegran@utwente.nl
@orestibanos
http://orestibanos.com/
19-Apr-18 1
Biodata analysis in the overall “biopicture”
19-Apr-18 2
Lectures 1, 2, 3
Lecture 4 (last week)Lecture 5 (today)
Biosignal acquisition
Biosignal processingBiodata analysis
Learning Objectives
At the end of this course you should be able to:
 Explain the concept of biodata and
enumerate some examples of types
of biodata
 Identify the different stages of the
biodata analysis chain, as well as
the purpose of each step
 Apply some regular biodata
segmentation techniques
 Utilise common biodata feature
extraction and selection techniques
 Employ typical biodata classification
techniques and use metrics to
evaluate their performance
19-Apr-18 3
Outline
 Biodata definition
 Biodata analysis chain
 Segmentation
 Feature extraction
 Feature selection
 Classification
 Performance evaluation
19-Apr-18 4
Outline
 Biodata definition
 Biodata analysis chain
 Segmentation
 Feature extraction
 Feature selection
 Classification
 Performance evaluation
19-Apr-18 5
Biodata: definition
 Data: is a collection or set of qualitative or
quantitative variables normally represented
through numbers, characters or symbols
 Biodata (also biomedical data, biological data):
collection of data specifically related to or
describing biological systems or processes
 Different “levels” of data
 Signal
 Features
 Categories
19-Apr-18 6
There are
more specific
definitions!
Biodata: examples
19-Apr-18 7
Genomic data
Kinematics data
Physiological data
Emotional data
Cognitive data
…
Motion data
19-Apr-18 8
 Body motion data:
 Different sensing technologies to track body movements
 Video: Kinect, Vicon
 EMG: MYO
 Inertial: Xsens, Shimmer, Smartphone/watch
 Acceleration:
 tridimensional signal (x, y, z)
 measures typically range from -2g to 2g (g=9.8m/s2) for
daily activities
 sampling frequency around 50Hz
 Multiple applications: Health
Abnormal
behavior
detection
Proactive
Assistance
Labour risk
prevention
Wellness
Sports
Gaming
Self-portrait portrait (Selfie), biodata?
19-Apr-18 9
Two weeks ago
you said…
Self-portrait portrait (Selfie), biodata?
19-Apr-18 10
And the answer
was…
(Medical) selfie data
19-Apr-18 11
Seborrheic
Dermatitis
Allergies,
fatigueConjunctivitis
Moles, skin diseases
Burns
Tongue
infections
(Medical) selfie data
19-Apr-18 12
Outline
 Biodata definition
 Biodata analysis chain
 Segmentation
 Feature extraction
 Feature selection
 Classification
 Performance evaluation
19-Apr-18 13
Biodata interpretation: not an easy job…
 Example (EEG signals)
19-Apr-18 14
Just
0.2 seconds
of data!!
Biodata interpretation: not an easy job…
 Medical experts cannot “digest” the enormous
amount of biodata generated by people
 Examples
 Breathing ~100K events/day
 Heart beats ~ 1M samples/day
 Motion ~100M samples/day
 EEG ~100M samples/day
19-Apr-18 15
How to make sense of
these gobs of data?
Biodata analysis chain
 Multistage process combining computational
techniques to automatically extract information
and develop decisions on a given data set
19-Apr-18 16
S = data source (sensor)
si = segment of data
u = raw/unprocessed data
f(si) = feature vector
p = preprocessed data
ci = class/label
Data acquisition and preprocessing
19-Apr-18 17
 Data acquisition refers here to the process of
measurement and digitisation of the biological
phenomenon (Lectures 1, 2, 3)
 Measurement and transduction
 Sampling
 Amplification
 Analog to digital conversion
 Data preprocessing refers here to the preparation
of the biodata for its posterior processing and
analysis (Lectures 4 and 5)
 Removal of artifacts
 Denoising
 Domain transformation
 Downsampling (decimation) / upsampling (interpolation)
Outline
 Biodata definition
 Biodata analysis chain
 Segmentation
 Feature extraction
 Feature selection
 Classification
 Performance evaluation
19-Apr-18 18
Segmentation
19-Apr-18 19
 Process to divide the biosignal or data into
smaller time segments
 The segmentation process is frequently called
“windowing” as each segment represents a data
window or frame
 In real-time applications, windows are defined
concurrently with data acquisition and processing,
so data streams can be effectively analysed “on-
the-fly”
Segmentation
19-Apr-18 20
 Sliding window
 Signals are split
into windows of a
fixed size and with
no inter-window
gaps
 An overlap
between adjacent
windows is
sometimes
tolerated
 Most widely used
approach
Window 1 Window 3 Window 5 Window 7
Window 1 Window 2 Window 3 Window 4
Fixed
window size
Window 2 Window 4 Window 6
Non-overlapingOverlaping
Segmentation
19-Apr-18 21
 Event-defined window
 The segment start and
end is defined by a
detected event
 Additional processing is
required to identify the
events of interest
 Example: toe offs and
heel strikes based on
the differentiation of the
acceleration signal
(derivative)
Data windows (normally)
of variable size
Segmentation
19-Apr-18 22
 Class-defined window
 The window start and
end is defined by a
change in the context
or class (also spotting)
 Example: activity
transition detected from
significant variations in
the energy or statistical
properties of the
acceleration signal
(e.g., variance)
Data windows (normally)
of variable size
Outline
 Biodata definition
 Biodata analysis chain
 Segmentation
 Feature extraction
 Feature selection
 Classification
 Performance evaluation
19-Apr-18 23
Featuring or characterisation
19-Apr-18 24
 How do you differentiate between these two
persons? What do they have in common?
Featuring or characterisation
19-Apr-18 25
 Sometimes it becomes difficult to tell…
Feature extraction
19-Apr-18 26
 Process of (numerically) characterising or
transforming raw data into more descriptive or
informative data
 Intended to facilitate the subsequent learning and
generalization steps, and in some cases lead to
better human interpretations
Location=prefrontal,
Size=3cm,
Density=60g/cm3, …
Feature extraction
19-Apr-18 27
 Time-domain features: statistical values derived
directly from data window
 Examples:
 Max
 Min
 Mean
 Median
 Variance
 Skewness
 Kurtosis
MATLAB: max, min, mean, median, var, skewness, kurtosis
0 0.5 1 1.5 2 2.5 3 3.5 4
Time (s)
-25
-20
-15
-10
-5
0
5
10
15
Acceleration(m/s2)
X-axis acceleration signal (JUMPING)
0 0.5 1 1.5 2 2.5 3 3.5 4
Time (s)
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
Acceleration(m/s2)
X-axis acceleration signal (WALKING)
0 0.5 1 1.5 2 2.5 3 3.5 4
Time (s)
-3.4
-3.3
-3.2
-3.1
-3
-2.9
-2.8
-2.7
-2.6
Acceleration(m/s2)
X-axis acceleration signal (STANDING)
Feature extraction
19-Apr-18 28
 Frequency-domain features: derived from a
transformed version of the data window in the
frequency domain
 Examples:
 Fundamental frequency
 N-order harmonics
 Mean/Median/Mode frequency
 Spectral power/energy
 Entropy
 Cepstrum coefficients
MATLAB: fft, pwelch, meanfreq, medfreq, rceps
0 5 10 15 20 25
Frequency (Hz)
0
200
400
600
800
1000
1200
1400
FFTMagnitude
X-axis acceleration signal (JUMPING)
0 5 10 15 20 25
Frequency (Hz)
0
20
40
60
80
100
120
140
160
FFTMagnitude
X-axis acceleration signal (STANDING)
0 5 10 15 20 25
Frequency (Hz)
0
20
40
60
80
100
120
140
160
180
200
FFTMagnitude
X-axis acceleration signal (WALKING)
Feature extraction
19-Apr-18 29
 Process of (numerically) characterising or
transforming raw data into more informative data
 The outcome of the feature extraction process is
normally a feature matrix
 Rows represent each data instance, chunk or segment
 Columns refer to the mathematical function (feature)
𝟎. 𝟏𝟖 𝟎. 𝟑𝟓
−𝟎. 𝟐𝟔 𝟎. 𝟏𝟓
−𝟎. 𝟎𝟓
−𝟎. 𝟏𝟗
𝟎. 𝟐𝟏
𝟎. 𝟏𝟖
Feature
matrix
F1: Mean F2: Variance
Feature extraction
19-Apr-18 30
 Feature space:
 Total number of features
extracted from the data
 Normally described as an array
(also known as feature matrix) in
which rows represent each
instance and columns the
feature type
 The dimensions (D) of the
feature space are given by the
number of features (N)
MATLAB: scatter
0 1 2 3
Mean
0
0.5
1
1.5
2
2.5
3
Variance
Class A
Class B
0.18 0.55
0.26 0.15
0.15
2.13
2.86
2.58
0.85
2.62
2.35
2.51
Feature
matrix
Mean Variance
0 0.5 1 1.5 2 2.5 3
Mean
0
0.5
1
1.5
2
2.5
3
Variance
Sitting
Climbing
Feature
space
Outline
 Biodata definition
 Biodata analysis chain
 Segmentation
 Feature extraction
 Feature selection
 Classification
 Performance evaluation
19-Apr-18 31
Features of “relevance”
19-Apr-18 32
 Relevant features can be
individually irrelevant
 A helpful feature may be
irrelevant by itself
 E.g.: the characteristic “being a
human” when comparing two
people vs. when comparing
animals
 Two individually irrelevant
features may become relevant
when used in combination
 E.g.: “row” and “column” in a
chess game when comparing the
color of a given position vs. using
either row or column solely
Feature selection
19-Apr-18 33
 Process to select relevant and
informative features
 Different motivations
 General data reduction: limit storage
requirements and increase algorithm
speed
 Feature set reduction: save resources in
the next round of data collection or
during its utilisation
 Performance improvement: gain in
predictive accuracy
 Data understanding: acquire knowledge
about the process that generated the
data or simply visualise the data
Feature selection
19-Apr-18 34
 Visualising the feature space can help determining which
features (or combination thereof) are most discriminative
 Hyperdimensional features spaces (#features > 3) need
to be reduced for a proper visualisation (e.g., PCA, ICA)
MATLAB: scatter3, pca, biplot, ica
-1
-0.5
0
0
0.5
-2
1
-2
1.5
Mean(accelerationZ)
2
-4 -3
Feature space representation
Mean (accelerationY)
2.5
-4
3
Mean (accelerationX)
-6
3.5
-5
-8
-6
-10 -7
Standing
Walking
Jumping
Do not trust statistics alone,
visualise your data!
Feature selection
19-Apr-18 35
 There are several feature ranking and selection methods
MATLAB: rankfeatures
 Filter methods:
 select variables regardless of the
classification model model (analyses
intrinsic properties of data)
 particularly effective in computation time
 robust to overfitting (excessive model
complexity, i.e., too many parameters
relative to the number of samples)
 Ranking feature selection:
 selects a subset of features according to
a statistical separability criteria (e.g., t-
test, ANOVA)
Set of all
features
Selecting
the best
subset
Learning
algorithm
Performance
evaluation
0.4 1 3.3 1
2.3 1 3.2 3
0.4
2.2
2.6
2.2
1
1
1
1
3.1
9.8
9.4
9.7
2
3
1
2
Class “Sitting”
Class “Climbing”
F1 F2 F3 F4
a) F1>F3>F4>F2?
b) F1>F3>F2>F4?
c) F3>F1>F4>F2?
d) F4>F2>F3>F1?
Go to go.voxvote.com
Insert the code PIN: 99020
Feature selection
19-Apr-18 36
 There are several feature ranking and selection methods
MATLAB: sequentialfs
 Wrapper methods:
 allow to detect the possible
interactions between features
 significant computation time for large
sets of features and prone to
overfitting
 Sequential feature selection:
 selects a subset of features from the
feature matrix that predict best the
output classes by iteratively selecting
features until there is no improvement in
prediction
Set of all
features
Selecting the best subset
Learning
algorithm
Performance
evaluation
Generate
a subset
0.4 1 3.3 1
2.3 1 3.2 3
0.4
2.2
2.6
2.2
1
1
1
1
3.1
9.8
9.4
9.7
2
3
1
2
Class “Sitting”
Class “Climbing”
F1 F2 F3 F4
a) F1>F2>F3>F4?
b) F2>F3>F1>F4?
c) F3>F1>F4>F2?
d) F4>F2>F3>F1?
Go to go.voxvote.com
Insert the code PIN: 99020
Outline
 Biodata definition
 Biodata analysis chain
 Segmentation
 Feature extraction
 Feature selection
 Classification
 Performance evaluation
19-Apr-18 37
Classification
19-Apr-18 38
 Problem of identifying to
which of a set of
categories or classes a
new observation belongs
 The classification model is
based on a training set of
data containing
observations (or
instances) whose
category membership is
(normally) known
0 1 2 3
Mean
0
0.5
1
1.5
2
2.5
3
Variance Class A
Class B
0.18 0.55
0.26 0.15
0.15
2.13
2.86
2.58
0.85
2.62
2.35
2.51
Feature
matrix
Mean Variance
Classification
boundary
0 0.5 1 1.5 2 2.5 3
Mean
0
0.5
1
1.5
2
2.5
3
Variance
Sitting
Climbing
Classification
19-Apr-18 39
 Types:
 Supervised
 E.g., decision tree
 Unsupervised
 E.g., clustering
 One size does not fit
all: the choice of
classifier is subject to
a trade-off between
complexity and
computational
resources
0 1 2 3
Mean
0
0.5
1
1.5
2
2.5
3
Variance
Class A
Class B
0.18 0.55
0.26 0.15
0.15
2.13
2.86
2.58
0.85
2.62
2.35
2.51
0 1 2 3
Mean
0
0.5
1
1.5
2
2.5
3
Variance
Class A
Class B
0.18 0.55
0.26 0.15
0.15
2.13
2.86
2.58
0.85
2.62
2.35
2.51
𝑨
𝑨
𝑨
𝑩
𝑩
𝑩
?
?
?
?
?
?
Supervised
Unsupervised
0 0.5 1 1.5 2 2.5 3
Mean
0
0.5
1
1.5
2
2.5
3
Variance
Sitting
Climbing
Classification
19-Apr-18 40
 Classification process:
 Training/learning
 Before operation the classification
model has to be trained (created)
 The model parameters are learned
from the training data and as to
minimise the classification error
 Example:
 In a decision tree, nodes
(conditions) and branches (decision
propagation) need to be defined
 The conditions are optimised as to
maximise the distance between
classes
0.18 0.55
0.26 0.15
0.15
2.13
2.86
2.58
0.85
2.62
2.35
2.51
𝑆𝑖𝑡𝑡𝑖𝑛𝑔
𝑆𝑖𝑡𝑡𝑖𝑛𝑔
𝑆𝑖𝑡𝑡𝑖𝑛𝑔
𝐶𝑙𝑖𝑚𝑏𝑖𝑛𝑔
𝐶𝑙𝑖𝑚𝑏𝑖𝑛𝑔
𝐶𝑙𝑖𝑚𝑏𝑖𝑛𝑔
+
Mean Variance
Mean < 1.2
Sitting Climbing
Class
MATLAB: fitctree
Classification
19-Apr-18 41
 Classification process:
 Classification/prediction
 Once the model is trained, it can
be used to categorise unseen
new instances into specific
classes
 The outputs of the classification
correspond to the inferred class
or categories
 Example:
 In a decision tree, the conditions
(nodes) are evaluated and the
applicable path followed up to
reach a conclusion (class)
0.52 −0.25
1.38 9.15
2.31
0.19
5.67
0.12
𝑆𝑖𝑡𝑡𝑖𝑛𝑔
𝐶𝑙𝑖𝑚𝑏𝑖𝑛𝑔
𝐶𝑙𝑖𝑚𝑏𝑖𝑛𝑔
𝑆𝑖𝑡𝑡𝑖𝑛𝑔
Mean < 1.2
Sitting Climbing
MATLAB: predict
Outline
 Biodata definition
 Biodata analysis chain
 Segmentation
 Feature extraction
 Feature selection
 Classification
 Performance evaluation
19-Apr-18 42
Performance evaluation
19-Apr-18 43
 Evaluating the performance of the classifier
(generally, the complete analysis chain) is
crucial to estimate the categorisation
capabilities of the system
 The performance evaluation
is normally conducted
during the design phase
 Classification performance
depends greatly on the
characteristics of the data to
be classified
 There is no single classifier
that works best on all given
problems
Performance evaluation
19-Apr-18 44
 Performance metrics
 Decision table (confusion matrix)
 Table layout that allows visualization
of the performance of a given
algorithm or classification model
 Each column of the matrix represents
the instances in a predicted class
while each row represents the
instances in an actual class
MATLAB: confusionmat, plotconfusion
𝑆𝑖𝑡𝑡𝑖𝑛𝑔
𝐶𝑙𝑖𝑚𝑏𝑖𝑛𝑔
𝑆𝑖𝑡𝑡𝑖𝑛𝑔
𝑆𝑖𝑡𝑡𝑖𝑛𝑔
Actual
class
Classified
class
Sitting Climbing
Sitting 2 1
Climbing 0 1
Classified class
Actualclass
Performance evaluation
19-Apr-18 45
 Performance metrics
 Accuracy (acc)
 Proportion of correct classifications
with respect to the total number of
classified instances or observations
MATLAB: classperf
𝑆𝑖𝑡𝑡𝑖𝑛𝑔
𝐶𝑙𝑖𝑚𝑏𝑖𝑛𝑔
𝑆𝑖𝑡𝑡𝑖𝑛𝑔
𝑆𝑖𝑡𝑡𝑖𝑛𝑔
Actual
class
Classified
class
𝒂𝒄𝒄 =
𝟏 + 𝟏 + 𝟎 + 𝟏
𝟒
= 𝟎. 𝟕𝟓  75%
Sitting Climbing
Sitting 2 1
Climbing 0 1
Classified class
Actualclass
Performance evaluation
19-Apr-18 46
 Experimental data is normally scarce and gives
insight on a sole scenario/situation
 Cross-validation: technique for assessing how
the results of a statistical classifier will generalize
to an independent data set (observations)
MATLAB: crossvalind, cvpartition, crossval
 Leave-one-out cross-validation (LOOCV)
 One observation is left out for validation
and remaining ones are used for training
𝟎. 𝟓𝟐 𝟎. 𝟐𝟓
𝟏. 𝟑𝟖 𝟗. 𝟏𝟓
𝟐. 𝟑𝟏
𝟎. 𝟏𝟗
𝟓. 𝟔𝟕
𝟎. 𝟏𝟐
Round 1
0.52 0.25
1.38 9.15
2.31
0.19
5.67
0.12
Round 2
0.52 0.25
1.38 9.15
2.31
0.19
5.67
0.12
Round 3
0.52 0.25
1.38 9.15
2.31
0.19
5.67
0.12
Round 4
0.52 0.25
1.38 9.15
2.31
0.19
5.67
0.12
Experimental
data
Validation set
Training set
acc1
Validation
accuracy
acc2 acc3 acc4
Final accuracy = average(acc_i) ∀ i
Performance evaluation
19-Apr-18 47
 Experimental data is normally scarce and gives
insight on a sole scenario/situation
 Cross-validation: technique for assessing how
the results of a statistical classifier will generalize
to an independent data set (observations)
MATLAB: crossvalind, cvpartition, crossval
 K-fold cross-validation (K-fold CV)
 Experimental data set is split into K folds
 K-1 folds are used for training
 The remaining fold is used for testing
 The process is repeated K times for each split
𝟎. 𝟓𝟐 𝟎. 𝟐𝟓
𝟏. 𝟑𝟖 𝟗. 𝟏𝟓
𝟐. 𝟑𝟏
𝟎. 𝟏𝟗
𝟓. 𝟔𝟕
𝟎. 𝟏𝟐
Experimental
data
Validation set
Training set
Round 2
0.52 0.25
1.38 9.15
2.31
0.19
5.67
0.12
Round 1
0.52 0.25
1.38 9.15
2.31
0.19
5.67
0.12
2-fold CV
75% 100%Validation
accuracy
Final accuracy = (75%+100%)/2 = 87.5%
References
 Biomedical Signal Processing and Analysis:
 Principal references:
 Mitchell, T. M., Machine Learning. McGraw-Hill, 1997 (Chapter 3)
 Rangayyan, R. M. Biomedical Signal Analysis: A Case-Study Approach. New
York: IEEE Press, 2002 (Chapters 8-9)
 Preece, Stephen J., Goulermas, John Y., Kenney, Laurence P. J., Howard, Dave,
Meijer, K., Crompton, R., et al. (2009). Activity identification using body-mounted
sensors – a review of classification techniques. Physiological Measurement,
30(4), R1–R33
 Other references:
 Bishop, C. M., Pattern Recognition and Machine Learning. Springer, 2006
 Bulling, A.; Blanke, U.; Schiele, B. A Tutorial on Human Activity Recognition Using
Body-worn Inertial Sensors. ACM Comput. Surv. 2014, 46, 1–33.
19-Apr-18 48
References
 Examples of open-access biodata sets
(http://orestibanos.com/datasets.htm)
19-Apr-18 49

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Biodata analysis

  • 1. Smart Tech: Biosignals & Medical Electronics Biodata analysis Oresti Banos June 2nd, 2017 o.banoslegran@utwente.nl @orestibanos http://orestibanos.com/ 19-Apr-18 1
  • 2. Biodata analysis in the overall “biopicture” 19-Apr-18 2 Lectures 1, 2, 3 Lecture 4 (last week)Lecture 5 (today) Biosignal acquisition Biosignal processingBiodata analysis
  • 3. Learning Objectives At the end of this course you should be able to:  Explain the concept of biodata and enumerate some examples of types of biodata  Identify the different stages of the biodata analysis chain, as well as the purpose of each step  Apply some regular biodata segmentation techniques  Utilise common biodata feature extraction and selection techniques  Employ typical biodata classification techniques and use metrics to evaluate their performance 19-Apr-18 3
  • 4. Outline  Biodata definition  Biodata analysis chain  Segmentation  Feature extraction  Feature selection  Classification  Performance evaluation 19-Apr-18 4
  • 5. Outline  Biodata definition  Biodata analysis chain  Segmentation  Feature extraction  Feature selection  Classification  Performance evaluation 19-Apr-18 5
  • 6. Biodata: definition  Data: is a collection or set of qualitative or quantitative variables normally represented through numbers, characters or symbols  Biodata (also biomedical data, biological data): collection of data specifically related to or describing biological systems or processes  Different “levels” of data  Signal  Features  Categories 19-Apr-18 6 There are more specific definitions!
  • 7. Biodata: examples 19-Apr-18 7 Genomic data Kinematics data Physiological data Emotional data Cognitive data …
  • 8. Motion data 19-Apr-18 8  Body motion data:  Different sensing technologies to track body movements  Video: Kinect, Vicon  EMG: MYO  Inertial: Xsens, Shimmer, Smartphone/watch  Acceleration:  tridimensional signal (x, y, z)  measures typically range from -2g to 2g (g=9.8m/s2) for daily activities  sampling frequency around 50Hz  Multiple applications: Health Abnormal behavior detection Proactive Assistance Labour risk prevention Wellness Sports Gaming
  • 9. Self-portrait portrait (Selfie), biodata? 19-Apr-18 9 Two weeks ago you said…
  • 10. Self-portrait portrait (Selfie), biodata? 19-Apr-18 10 And the answer was…
  • 11. (Medical) selfie data 19-Apr-18 11 Seborrheic Dermatitis Allergies, fatigueConjunctivitis Moles, skin diseases Burns Tongue infections
  • 13. Outline  Biodata definition  Biodata analysis chain  Segmentation  Feature extraction  Feature selection  Classification  Performance evaluation 19-Apr-18 13
  • 14. Biodata interpretation: not an easy job…  Example (EEG signals) 19-Apr-18 14 Just 0.2 seconds of data!!
  • 15. Biodata interpretation: not an easy job…  Medical experts cannot “digest” the enormous amount of biodata generated by people  Examples  Breathing ~100K events/day  Heart beats ~ 1M samples/day  Motion ~100M samples/day  EEG ~100M samples/day 19-Apr-18 15 How to make sense of these gobs of data?
  • 16. Biodata analysis chain  Multistage process combining computational techniques to automatically extract information and develop decisions on a given data set 19-Apr-18 16 S = data source (sensor) si = segment of data u = raw/unprocessed data f(si) = feature vector p = preprocessed data ci = class/label
  • 17. Data acquisition and preprocessing 19-Apr-18 17  Data acquisition refers here to the process of measurement and digitisation of the biological phenomenon (Lectures 1, 2, 3)  Measurement and transduction  Sampling  Amplification  Analog to digital conversion  Data preprocessing refers here to the preparation of the biodata for its posterior processing and analysis (Lectures 4 and 5)  Removal of artifacts  Denoising  Domain transformation  Downsampling (decimation) / upsampling (interpolation)
  • 18. Outline  Biodata definition  Biodata analysis chain  Segmentation  Feature extraction  Feature selection  Classification  Performance evaluation 19-Apr-18 18
  • 19. Segmentation 19-Apr-18 19  Process to divide the biosignal or data into smaller time segments  The segmentation process is frequently called “windowing” as each segment represents a data window or frame  In real-time applications, windows are defined concurrently with data acquisition and processing, so data streams can be effectively analysed “on- the-fly”
  • 20. Segmentation 19-Apr-18 20  Sliding window  Signals are split into windows of a fixed size and with no inter-window gaps  An overlap between adjacent windows is sometimes tolerated  Most widely used approach Window 1 Window 3 Window 5 Window 7 Window 1 Window 2 Window 3 Window 4 Fixed window size Window 2 Window 4 Window 6 Non-overlapingOverlaping
  • 21. Segmentation 19-Apr-18 21  Event-defined window  The segment start and end is defined by a detected event  Additional processing is required to identify the events of interest  Example: toe offs and heel strikes based on the differentiation of the acceleration signal (derivative) Data windows (normally) of variable size
  • 22. Segmentation 19-Apr-18 22  Class-defined window  The window start and end is defined by a change in the context or class (also spotting)  Example: activity transition detected from significant variations in the energy or statistical properties of the acceleration signal (e.g., variance) Data windows (normally) of variable size
  • 23. Outline  Biodata definition  Biodata analysis chain  Segmentation  Feature extraction  Feature selection  Classification  Performance evaluation 19-Apr-18 23
  • 24. Featuring or characterisation 19-Apr-18 24  How do you differentiate between these two persons? What do they have in common?
  • 25. Featuring or characterisation 19-Apr-18 25  Sometimes it becomes difficult to tell…
  • 26. Feature extraction 19-Apr-18 26  Process of (numerically) characterising or transforming raw data into more descriptive or informative data  Intended to facilitate the subsequent learning and generalization steps, and in some cases lead to better human interpretations Location=prefrontal, Size=3cm, Density=60g/cm3, …
  • 27. Feature extraction 19-Apr-18 27  Time-domain features: statistical values derived directly from data window  Examples:  Max  Min  Mean  Median  Variance  Skewness  Kurtosis MATLAB: max, min, mean, median, var, skewness, kurtosis 0 0.5 1 1.5 2 2.5 3 3.5 4 Time (s) -25 -20 -15 -10 -5 0 5 10 15 Acceleration(m/s2) X-axis acceleration signal (JUMPING) 0 0.5 1 1.5 2 2.5 3 3.5 4 Time (s) -9 -8 -7 -6 -5 -4 -3 -2 -1 0 Acceleration(m/s2) X-axis acceleration signal (WALKING) 0 0.5 1 1.5 2 2.5 3 3.5 4 Time (s) -3.4 -3.3 -3.2 -3.1 -3 -2.9 -2.8 -2.7 -2.6 Acceleration(m/s2) X-axis acceleration signal (STANDING)
  • 28. Feature extraction 19-Apr-18 28  Frequency-domain features: derived from a transformed version of the data window in the frequency domain  Examples:  Fundamental frequency  N-order harmonics  Mean/Median/Mode frequency  Spectral power/energy  Entropy  Cepstrum coefficients MATLAB: fft, pwelch, meanfreq, medfreq, rceps 0 5 10 15 20 25 Frequency (Hz) 0 200 400 600 800 1000 1200 1400 FFTMagnitude X-axis acceleration signal (JUMPING) 0 5 10 15 20 25 Frequency (Hz) 0 20 40 60 80 100 120 140 160 FFTMagnitude X-axis acceleration signal (STANDING) 0 5 10 15 20 25 Frequency (Hz) 0 20 40 60 80 100 120 140 160 180 200 FFTMagnitude X-axis acceleration signal (WALKING)
  • 29. Feature extraction 19-Apr-18 29  Process of (numerically) characterising or transforming raw data into more informative data  The outcome of the feature extraction process is normally a feature matrix  Rows represent each data instance, chunk or segment  Columns refer to the mathematical function (feature) 𝟎. 𝟏𝟖 𝟎. 𝟑𝟓 −𝟎. 𝟐𝟔 𝟎. 𝟏𝟓 −𝟎. 𝟎𝟓 −𝟎. 𝟏𝟗 𝟎. 𝟐𝟏 𝟎. 𝟏𝟖 Feature matrix F1: Mean F2: Variance
  • 30. Feature extraction 19-Apr-18 30  Feature space:  Total number of features extracted from the data  Normally described as an array (also known as feature matrix) in which rows represent each instance and columns the feature type  The dimensions (D) of the feature space are given by the number of features (N) MATLAB: scatter 0 1 2 3 Mean 0 0.5 1 1.5 2 2.5 3 Variance Class A Class B 0.18 0.55 0.26 0.15 0.15 2.13 2.86 2.58 0.85 2.62 2.35 2.51 Feature matrix Mean Variance 0 0.5 1 1.5 2 2.5 3 Mean 0 0.5 1 1.5 2 2.5 3 Variance Sitting Climbing Feature space
  • 31. Outline  Biodata definition  Biodata analysis chain  Segmentation  Feature extraction  Feature selection  Classification  Performance evaluation 19-Apr-18 31
  • 32. Features of “relevance” 19-Apr-18 32  Relevant features can be individually irrelevant  A helpful feature may be irrelevant by itself  E.g.: the characteristic “being a human” when comparing two people vs. when comparing animals  Two individually irrelevant features may become relevant when used in combination  E.g.: “row” and “column” in a chess game when comparing the color of a given position vs. using either row or column solely
  • 33. Feature selection 19-Apr-18 33  Process to select relevant and informative features  Different motivations  General data reduction: limit storage requirements and increase algorithm speed  Feature set reduction: save resources in the next round of data collection or during its utilisation  Performance improvement: gain in predictive accuracy  Data understanding: acquire knowledge about the process that generated the data or simply visualise the data
  • 34. Feature selection 19-Apr-18 34  Visualising the feature space can help determining which features (or combination thereof) are most discriminative  Hyperdimensional features spaces (#features > 3) need to be reduced for a proper visualisation (e.g., PCA, ICA) MATLAB: scatter3, pca, biplot, ica -1 -0.5 0 0 0.5 -2 1 -2 1.5 Mean(accelerationZ) 2 -4 -3 Feature space representation Mean (accelerationY) 2.5 -4 3 Mean (accelerationX) -6 3.5 -5 -8 -6 -10 -7 Standing Walking Jumping Do not trust statistics alone, visualise your data!
  • 35. Feature selection 19-Apr-18 35  There are several feature ranking and selection methods MATLAB: rankfeatures  Filter methods:  select variables regardless of the classification model model (analyses intrinsic properties of data)  particularly effective in computation time  robust to overfitting (excessive model complexity, i.e., too many parameters relative to the number of samples)  Ranking feature selection:  selects a subset of features according to a statistical separability criteria (e.g., t- test, ANOVA) Set of all features Selecting the best subset Learning algorithm Performance evaluation 0.4 1 3.3 1 2.3 1 3.2 3 0.4 2.2 2.6 2.2 1 1 1 1 3.1 9.8 9.4 9.7 2 3 1 2 Class “Sitting” Class “Climbing” F1 F2 F3 F4 a) F1>F3>F4>F2? b) F1>F3>F2>F4? c) F3>F1>F4>F2? d) F4>F2>F3>F1? Go to go.voxvote.com Insert the code PIN: 99020
  • 36. Feature selection 19-Apr-18 36  There are several feature ranking and selection methods MATLAB: sequentialfs  Wrapper methods:  allow to detect the possible interactions between features  significant computation time for large sets of features and prone to overfitting  Sequential feature selection:  selects a subset of features from the feature matrix that predict best the output classes by iteratively selecting features until there is no improvement in prediction Set of all features Selecting the best subset Learning algorithm Performance evaluation Generate a subset 0.4 1 3.3 1 2.3 1 3.2 3 0.4 2.2 2.6 2.2 1 1 1 1 3.1 9.8 9.4 9.7 2 3 1 2 Class “Sitting” Class “Climbing” F1 F2 F3 F4 a) F1>F2>F3>F4? b) F2>F3>F1>F4? c) F3>F1>F4>F2? d) F4>F2>F3>F1? Go to go.voxvote.com Insert the code PIN: 99020
  • 37. Outline  Biodata definition  Biodata analysis chain  Segmentation  Feature extraction  Feature selection  Classification  Performance evaluation 19-Apr-18 37
  • 38. Classification 19-Apr-18 38  Problem of identifying to which of a set of categories or classes a new observation belongs  The classification model is based on a training set of data containing observations (or instances) whose category membership is (normally) known 0 1 2 3 Mean 0 0.5 1 1.5 2 2.5 3 Variance Class A Class B 0.18 0.55 0.26 0.15 0.15 2.13 2.86 2.58 0.85 2.62 2.35 2.51 Feature matrix Mean Variance Classification boundary 0 0.5 1 1.5 2 2.5 3 Mean 0 0.5 1 1.5 2 2.5 3 Variance Sitting Climbing
  • 39. Classification 19-Apr-18 39  Types:  Supervised  E.g., decision tree  Unsupervised  E.g., clustering  One size does not fit all: the choice of classifier is subject to a trade-off between complexity and computational resources 0 1 2 3 Mean 0 0.5 1 1.5 2 2.5 3 Variance Class A Class B 0.18 0.55 0.26 0.15 0.15 2.13 2.86 2.58 0.85 2.62 2.35 2.51 0 1 2 3 Mean 0 0.5 1 1.5 2 2.5 3 Variance Class A Class B 0.18 0.55 0.26 0.15 0.15 2.13 2.86 2.58 0.85 2.62 2.35 2.51 𝑨 𝑨 𝑨 𝑩 𝑩 𝑩 ? ? ? ? ? ? Supervised Unsupervised 0 0.5 1 1.5 2 2.5 3 Mean 0 0.5 1 1.5 2 2.5 3 Variance Sitting Climbing
  • 40. Classification 19-Apr-18 40  Classification process:  Training/learning  Before operation the classification model has to be trained (created)  The model parameters are learned from the training data and as to minimise the classification error  Example:  In a decision tree, nodes (conditions) and branches (decision propagation) need to be defined  The conditions are optimised as to maximise the distance between classes 0.18 0.55 0.26 0.15 0.15 2.13 2.86 2.58 0.85 2.62 2.35 2.51 𝑆𝑖𝑡𝑡𝑖𝑛𝑔 𝑆𝑖𝑡𝑡𝑖𝑛𝑔 𝑆𝑖𝑡𝑡𝑖𝑛𝑔 𝐶𝑙𝑖𝑚𝑏𝑖𝑛𝑔 𝐶𝑙𝑖𝑚𝑏𝑖𝑛𝑔 𝐶𝑙𝑖𝑚𝑏𝑖𝑛𝑔 + Mean Variance Mean < 1.2 Sitting Climbing Class MATLAB: fitctree
  • 41. Classification 19-Apr-18 41  Classification process:  Classification/prediction  Once the model is trained, it can be used to categorise unseen new instances into specific classes  The outputs of the classification correspond to the inferred class or categories  Example:  In a decision tree, the conditions (nodes) are evaluated and the applicable path followed up to reach a conclusion (class) 0.52 −0.25 1.38 9.15 2.31 0.19 5.67 0.12 𝑆𝑖𝑡𝑡𝑖𝑛𝑔 𝐶𝑙𝑖𝑚𝑏𝑖𝑛𝑔 𝐶𝑙𝑖𝑚𝑏𝑖𝑛𝑔 𝑆𝑖𝑡𝑡𝑖𝑛𝑔 Mean < 1.2 Sitting Climbing MATLAB: predict
  • 42. Outline  Biodata definition  Biodata analysis chain  Segmentation  Feature extraction  Feature selection  Classification  Performance evaluation 19-Apr-18 42
  • 43. Performance evaluation 19-Apr-18 43  Evaluating the performance of the classifier (generally, the complete analysis chain) is crucial to estimate the categorisation capabilities of the system  The performance evaluation is normally conducted during the design phase  Classification performance depends greatly on the characteristics of the data to be classified  There is no single classifier that works best on all given problems
  • 44. Performance evaluation 19-Apr-18 44  Performance metrics  Decision table (confusion matrix)  Table layout that allows visualization of the performance of a given algorithm or classification model  Each column of the matrix represents the instances in a predicted class while each row represents the instances in an actual class MATLAB: confusionmat, plotconfusion 𝑆𝑖𝑡𝑡𝑖𝑛𝑔 𝐶𝑙𝑖𝑚𝑏𝑖𝑛𝑔 𝑆𝑖𝑡𝑡𝑖𝑛𝑔 𝑆𝑖𝑡𝑡𝑖𝑛𝑔 Actual class Classified class Sitting Climbing Sitting 2 1 Climbing 0 1 Classified class Actualclass
  • 45. Performance evaluation 19-Apr-18 45  Performance metrics  Accuracy (acc)  Proportion of correct classifications with respect to the total number of classified instances or observations MATLAB: classperf 𝑆𝑖𝑡𝑡𝑖𝑛𝑔 𝐶𝑙𝑖𝑚𝑏𝑖𝑛𝑔 𝑆𝑖𝑡𝑡𝑖𝑛𝑔 𝑆𝑖𝑡𝑡𝑖𝑛𝑔 Actual class Classified class 𝒂𝒄𝒄 = 𝟏 + 𝟏 + 𝟎 + 𝟏 𝟒 = 𝟎. 𝟕𝟓  75% Sitting Climbing Sitting 2 1 Climbing 0 1 Classified class Actualclass
  • 46. Performance evaluation 19-Apr-18 46  Experimental data is normally scarce and gives insight on a sole scenario/situation  Cross-validation: technique for assessing how the results of a statistical classifier will generalize to an independent data set (observations) MATLAB: crossvalind, cvpartition, crossval  Leave-one-out cross-validation (LOOCV)  One observation is left out for validation and remaining ones are used for training 𝟎. 𝟓𝟐 𝟎. 𝟐𝟓 𝟏. 𝟑𝟖 𝟗. 𝟏𝟓 𝟐. 𝟑𝟏 𝟎. 𝟏𝟗 𝟓. 𝟔𝟕 𝟎. 𝟏𝟐 Round 1 0.52 0.25 1.38 9.15 2.31 0.19 5.67 0.12 Round 2 0.52 0.25 1.38 9.15 2.31 0.19 5.67 0.12 Round 3 0.52 0.25 1.38 9.15 2.31 0.19 5.67 0.12 Round 4 0.52 0.25 1.38 9.15 2.31 0.19 5.67 0.12 Experimental data Validation set Training set acc1 Validation accuracy acc2 acc3 acc4 Final accuracy = average(acc_i) ∀ i
  • 47. Performance evaluation 19-Apr-18 47  Experimental data is normally scarce and gives insight on a sole scenario/situation  Cross-validation: technique for assessing how the results of a statistical classifier will generalize to an independent data set (observations) MATLAB: crossvalind, cvpartition, crossval  K-fold cross-validation (K-fold CV)  Experimental data set is split into K folds  K-1 folds are used for training  The remaining fold is used for testing  The process is repeated K times for each split 𝟎. 𝟓𝟐 𝟎. 𝟐𝟓 𝟏. 𝟑𝟖 𝟗. 𝟏𝟓 𝟐. 𝟑𝟏 𝟎. 𝟏𝟗 𝟓. 𝟔𝟕 𝟎. 𝟏𝟐 Experimental data Validation set Training set Round 2 0.52 0.25 1.38 9.15 2.31 0.19 5.67 0.12 Round 1 0.52 0.25 1.38 9.15 2.31 0.19 5.67 0.12 2-fold CV 75% 100%Validation accuracy Final accuracy = (75%+100%)/2 = 87.5%
  • 48. References  Biomedical Signal Processing and Analysis:  Principal references:  Mitchell, T. M., Machine Learning. McGraw-Hill, 1997 (Chapter 3)  Rangayyan, R. M. Biomedical Signal Analysis: A Case-Study Approach. New York: IEEE Press, 2002 (Chapters 8-9)  Preece, Stephen J., Goulermas, John Y., Kenney, Laurence P. J., Howard, Dave, Meijer, K., Crompton, R., et al. (2009). Activity identification using body-mounted sensors – a review of classification techniques. Physiological Measurement, 30(4), R1–R33  Other references:  Bishop, C. M., Pattern Recognition and Machine Learning. Springer, 2006  Bulling, A.; Blanke, U.; Schiele, B. A Tutorial on Human Activity Recognition Using Body-worn Inertial Sensors. ACM Comput. Surv. 2014, 46, 1–33. 19-Apr-18 48
  • 49. References  Examples of open-access biodata sets (http://orestibanos.com/datasets.htm) 19-Apr-18 49

Editor's Notes

  1. NOTES: ADDITIONAL TEXT:
  2. NOTES: Signal acquisition: Erik already introduced a little bit of it, and it will be explored in more detailed during the 5th and 6th lecture Signal processing: this is what we will see today Signal analysis (or data analysis): we will see this part next week Signal conditioning refers to acquisition, amplification, levelling Electrical biosignal conditioning will be explored in depth in Lecture 5 (Amplifiers in Electrophysiological Measurements) and Lecture 6 (Noise in Electrophysiological Measurements) ADDITIONAL TEXT: The signals reflect properties of their associated underlying biological systems, and their decoding has been found very helpful in explaining and identifying various pathological conditions. The decoding process is sometimes straightforward and may only involve very limited, manual effort such as visual inspection of the sig- nal on a paper print-out or computer screen. However, the complexity of a signal is often quite considerable, and, therefore, biomedical signal pro- cessing has become an indispensable tool for extracting clinically significant information hidden in the signal.
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  6. NOTES: Biodata has been traditionally used for describing biographical data, for example, a résumé Are signals different than data? No, signals are just a subcategory of what could be considered data. Example: ECG Data: ECG electrical signal Information: Heart rate (number of contractions per minute) Knowledge: Heart rate > 180 bpm determines an abnormal situation Knowledge: Heart rate > 100 bpm while resting determines an abnormal situation Wisdom: if Heart rate > 100 bpm while resting call an ambulance The boundaries between data, information, knowledge, wisdom are not always clear:  What is data to one person is information to someone else. http://searchdatamanagement.techtarget.com/feature/Defining-data-information-and-knowledge ADDITIONAL TEXT: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2814957/
  7. NOTES: Genomic data: information embedded into your genes, one of the fundations of the personalised medicine (i.e., you know that drugs are made to work for the population in general but they show different effects for each person; the idea is to create person-centric medicines so the drug works best and with minimum adverse effects) Emotional data: identification of emotional and mental states, for example, anger, fear, depression, stress Cognitive data: attention, memory and working memory, judgment  ADDITIONAL TEXT:
  8. NOTES: Vicon: reflective markers (tiny spots) EMG: more on a local/muscular level (intended for determining gestures) Many others: inclinometer, goniometers, skin temperature, galvanic skin response, GPS, etc. Health and wellness care but also in many other areas: Industry, Films,… ADDITIONAL TEXT:
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  12. NOTES: Photographic evidence could provide your doctor with vital information they might otherwise miss. Stacey Yepes (2014), a Canadian woman who used her mobile phone to capture an episode of symptoms that included slurred speech and facial paralysis, which ultimately lead to a diagnosis of a stroke. In this case, the medical selfie led to an accurate diagnosis that could well have saved a life ADDITIONAL TEXT:
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  15. NOTES: And you have to multiply this by the number of channels (e.g., in motion data – 3 for an accelerometer –; in EEG – 10 to 20, depending upon the number of scalp electrodes –) Imagine you use 10 channels and 8bits to encode this information, how many bytes would this result in? EEG 10x100MB --> 1GB per day! (This seems not that much, but imagine inspecting/reviewing such amount of data manually) ADDITIONAL TEXT:
  16. NOTES: Computational techniques ADDITIONAL TEXT:
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  19. NOTES: - Segmenting or partitioning things is something that you do more or less every day right, e.g., when you prepare a sandwich, but also when you divide your tasks or homework in blocks ADDITIONAL TEXT:
  20. NOTES: The signals are split into windows of a fixed size and with no inter-window gaps. An overlap between adjacent windows is tolerated for certain applications; however, this is less frequently used ADDITIONAL TEXT:
  21. NOTES: The signals are split into windows of a variable size The event detection could be based on time-domain changes but also on frequency-domain changes (remember the event detection section that you saw last week) ADDITIONAL TEXT:
  22. NOTES: The signals are split into windows of a variable size For the class-defined window think of an audio: each word or character could refer to a given context/category (sometimes in order to automatically identify the start and end what we focus on is the energy of that part, or some statistical properties that hold during the period the context takes place) ADDITIONAL TEXT:
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  24. NOTES: Question: How do you think you can differentiate these two guys (apart from the obvious)? Otherwise, what do you think happens in your brain when you see these two picture? Some features are measurable: skin color, eyes, haircut, age, some others are more subjective: craziness for example ADDITIONAL TEXT:
  25. NOTES: Question: How do you think you can differentiate these two guys (apart from the obvious)? Otherwise, what do you think happens in your brain when you see these two picture? Some features are measurable: skin color, eyes, haircut, age, some others are more subjective: craziness for example ADDITIONAL TEXT:
  26. NOTES: - is one of the key steps in the data analysis process, largely conditioning the success of any subsequent statistics or machine learning endeavor We have already seen some means for extracting features, e.g., RR distance is a feature, the HR is also a feature Density, size, location of a tumor (CT scan) Genetic/proteomic features (boolean, either it is activated or not) ADDITIONAL TEXT:
  27. NOTES: - MOD4 you took a full course on statistics, right? Skewness: measure of the asymmetry of the probability distribution of a real-valuedrandom variable about its mean Kurtosis: is a measure of the "tailedness" of the probability distribution of a real-valued random variable - Mean can be used to determine the orientation of an accelerometer during a resting state (based on the basic acceleration) ADDITIONAL TEXT:
  28. NOTES: Fundamental frequency: often referred to as simply as the fundamental, is defined as the lowest frequency of a periodic waveform Mean/Median/Mode frequency: statistics computed over the power spectrum of a time-domain signal Spectral power/energy: distribution of the signal energy Entropy: measurement of the amount of information in a given signal Cepstrum: the Inverse Fourier transform (IFT) of the logarithm of the estimated spectrum of a signal – rate of change in the different spectrum bands (used for voice identification, pitch detection) Some of these features are typically use in voice or speech recognition; ADDITIONAL TEXT:
  29. NOTES: - is one of the key steps in the data analysis process, largely conditioning the success of any subsequent statistics or machine learn- ing endeavor We have already seen some means for extracting features, e.g., RR distance is a feature, the HR is also a feature Density, size, location of a tumor (CT scan) Genetic/proteomic features (boolean, either it is activated or not) ADDITIONAL TEXT:
  30. NOTES: Problem of dimensionality: leads to the need of feature selection, not only for visualisation of the features but also for the posterior machine learning Well in some cases turns to be difficult to find a proper feature ADDITIONAL TEXT:
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  32. NOTES: Well in some cases turns to be difficult to find a proper feature ADDITIONAL TEXT:
  33. NOTES: - the good, if brief, is twice as good ADDITIONAL TEXT:
  34. NOTES: Question: the good, if brief, is twice as good Which feature do you think would be preferably selected here? c ADDITIONAL TEXT:
  35. NOTES: the good, if brief, is twice as good Which feature do you think would be preferably selected here? c ADDITIONAL TEXT:
  36. NOTES: the good, if brief, is twice as good Which feature do you think would be preferably selected here? c ADDITIONAL TEXT:
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  38. NOTES: ADDITIONAL TEXT: - BSPCNA Ch1.4
  39. NOTES: - There is a third category called semi-supervised in which part of the data is labelled (e.g., reinforcement learning) ADDITIONAL TEXT: - BSPCNA Ch1.4
  40. NOTES: Each path translates into a given rule Question: What would be the visual representation for this decision tree? See figure ADDITIONAL TEXT: - BSPCNA Ch1.4
  41. NOTES: - There is a third category called semi-supervised in which part of the data is labelled (e.g., reinforcement learning) ADDITIONAL TEXT: - BSPCNA Ch1.4
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  44. NOTES: A.k.a, Contingency table, Error matrix - Include a description of the Precision and Recall ADDITIONAL TEXT:
  45. NOTES: A.k.a, Contingency table, Error matrix - Include a description of the Precision and Recall ADDITIONAL TEXT:
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