We have seen that we can do machine learning on data that is in the nice “flat
file” format
• Rows are objects
• Columns are features
Taking a real problem and “massaging” it into this format is domain dependent,
but often the most fun part of machine learning.
Let see just one example….
Insect
ID
Abdomen
Length
1
2
3
4
5
6
7
8
9
10
2.7
8.0
0.9
1.1
5.4
2.9
6.1
0.5
8.3
8.1
Antennae Insect Class
Length
Grasshopper
5.5
9.1
4.7
3.1
8.5
1.9
6.6
1.0
6.6
4.7
Katydid
Grasshopper
Grasshopper
Katydid
Grasshopper
Katydid
Grasshopper
Katydid
Katydids
(Western Pipistrelle
(Parastrellus hesperus)
Photo by Michael Durham
A spectrogram of a bat call.
Western pipistrelle calls
We can easily measure two features of bat calls. Their
characteristic frequency and their call duration
Characteristic frequency
Call duration
Bat ID
Characteristic
frequency
1
49.7
Call duration
(ms)
5.5
Bat Species
Western pipistrelle
Classification
We have seen 2 classification techniques:
• Simple linear classifier, Nearest neighbor,.
Let us see two more techniques:
• Decision tree, Naïve Bayes
There are other techniques:
• Neural Networks, Support Vector Machines, … that we will not
consider..
I have a box of apples..
Pr(X = good) = p
then Pr(X = bad) = 1 − p
the entropy of X is given by
H(X)
1
0.5
0
binary entropy function
attains its maximum value
when p = 0.5
0
1
Decision Tree Classifier
10
9
8
7
6
5
4
3
2
1
Antenna Length
Ross Quinlan
Abdomen Length > 7.1?
yes
no
Antenna Length > 6.0?
1 2 3 4 5 6 7 8 9 10
Abdomen Length
Katydid
no
yes
Grasshopper
Katydid
Antennae shorter than body?
Yes
No
3 Tarsi?
Grasshopper
Yes
No
Foretiba has ears?
Cricket
Decision trees predate computers
Yes
Katydids
No
Camel Cricket
Decision Tree Classification
• Decision tree
–
–
–
–
A flow-chart-like tree structure
Internal node denotes a test on an attribute
Branch represents an outcome of the test
Leaf nodes represent class labels or class distribution
• Decision tree generation consists of two phases
– Tree construction
• At start, all the training examples are at the root
• Partition examples recursively based on selected attributes
– Tree pruning
• Identify and remove branches that reflect noise or outliers
• Use of decision tree: Classifying an unknown sample
– Test the attribute values of the sample against the decision tree
How do we construct the decision tree?
• Basic algorithm (a greedy algorithm)
– Tree is constructed in a top-down recursive divide-and-conquer manner
– At start, all the training examples are at the root
– Attributes are categorical (if continuous-valued, they can be discretized
in advance)
– Examples are partitioned recursively based on selected attributes.
– Test attributes are selected on the basis of a heuristic or statistical
measure (e.g., information gain)
• Conditions for stopping partitioning
– All samples for a given node belong to the same class
– There are no remaining attributes for further partitioning – majority
voting is employed for classifying the leaf
– There are no samples left
Information Gain as A Splitting Criteria
• Select the attribute with the highest information gain (information
gain is the expected reduction in entropy).
• Assume there are two classes, P and N
– Let the set of examples S contain p elements of class P and n elements of
class N
– The amount of information, needed to decide if an arbitrary example in S
belongs to P or N is defined as
 p 
p
 
E(S )  
log 2 
pn
 pn
0 log(0) is defined as 0
 n 
n

log 2 
pn
 pn
Information Gain in Decision Tree Induction
• Assume that using attribute A, a current set will be
partitioned into some number of child sets
• The encoding information that would be gained by
branching on A
Gain( A)  E (Current set )   E (all child sets )
Note: entropy is at its minimum if the collection of objects is completely uniform
Person
Homer
Marge
Bart
Lisa
Maggie
Abe
Selma
Otto
Krusty
Comic
Hair
Length
Weight
Age
Class
0”
10”
2”
6”
4”
1”
8”
10”
6”
250
150
90
78
20
170
160
180
200
36
34
10
8
1
70
41
38
45
M
F
M
F
F
M
F
M
M
8”
290
38
?
Entropy ( S )  
 p 
p
 
log 2 
pn
p

n


 n 
n

log 2 
pn
p

n


Entropy(4F,5M) = -(4/9)log2(4/9) - (5/9)log2(5/9)
= 0.9911
yes
no
Hair Length <= 5?
Let us try splitting
on Hair length
Gain( A)  E (Current set )   E (all child sets )
Gain(Hair Length <= 5) = 0.9911 – (4/9 * 0.8113 + 5/9 * 0.9710 ) = 0.0911
Entropy ( S )  
 p 
p
 
log 2 
pn
p

n


 n 
n

log 2 
pn
p

n


Entropy(4F,5M) = -(4/9)log2(4/9) - (5/9)log2(5/9)
= 0.9911
yes
no
Weight <= 160?
Let us try splitting
on Weight
Gain( A)  E (Current set )   E (all child sets )
Gain(Weight <= 160) = 0.9911 – (5/9 * 0.7219 + 4/9 * 0 ) = 0.5900
Entropy ( S )  
 p 
p
 
log 2 
pn
p

n


 n 
n

log 2 
pn
p

n


Entropy(4F,5M) = -(4/9)log2(4/9) - (5/9)log2(5/9)
= 0.9911
yes
no
age <= 40?
Let us try splitting
on Age
Gain( A)  E (Current set )   E (all child sets )
Gain(Age <= 40) = 0.9911 – (6/9 * 1 + 3/9 * 0.9183 ) = 0.0183
Of the 3 features we had, Weight
was best. But while people who
weigh over 160 are perfectly
classified (as males), the under 160
people are not perfectly
classified… So we simply recurse!
This time we find that we
can split on Hair length, and
we are done!
yes
yes
no
Weight <= 160?
no
Hair Length <= 2?
We need don’t need to keep the data
around, just the test conditions.
Weight <= 160?
yes
How would
these people
be classified?
no
Hair Length <= 2?
yes
Male
no
Female
Male
It is trivial to convert Decision
Trees to rules…
Weight <= 160?
yes
Hair Length <= 2?
yes
Male
no
Female
Rules to Classify Males/Females
If Weight greater than 160, classify as Male
Elseif Hair Length less than or equal to 2, classify as Male
Else classify as Female
no
Male
Once we have learned the decision tree, we don’t even need a computer!
This decision tree is attached to a medical machine, and is designed to help
nurses make decisions about what type of doctor to call.
Decision tree for a typical shared-care setting applying
the system for the diagnosis of prostatic obstructions.
PSA
= serum prostate-specific antigen levels
PSAD = PSA density
TRUS = transrectal ultrasound
Garzotto M et al. JCO 2005;23:4322-4329
The worked examples we have
seen were performed on small
datasets. However with small
datasets there is a great danger of
overfitting the data…
When you have few datapoints,
there are many possible splitting
rules that perfectly classify the
data, but will not generalize to
future datasets.
Yes
No
Wears green?
Female
Male
For example, the rule “Wears green?” perfectly classifies the data, so does
“Mothers name is Jacqueline?”, so does “Has blue shoes”…
Avoid Overfitting in Classification
• The generated tree may overfit the training data
– Too many branches, some may reflect anomalies due to
noise or outliers
– Result is in poor accuracy for unseen samples
• Two approaches to avoid overfitting
– Prepruning: Halt tree construction early—do not split a
node if this would result in the goodness measure falling
below a threshold
• Difficult to choose an appropriate threshold
– Postpruning: Remove branches from a “fully grown”
tree—get a sequence of progressively pruned trees
• Use a set of data different from the training data to
decide which is the “best pruned tree”
Which of the “Pigeon Problems” can be
solved by a Decision Tree?
1) Deep Bushy Tree
2) Useless
3) Deep Bushy Tree
10
9
8
7
6
5
4
3
2
1
1 2 3 4 5 6 7 8 9 10
The Decision Tree
has a hard time with
correlated attributes
10
9
8
7
6
5
4
3
2
1
100
90
80
70
60
50
40
30
20
10
10 20 30 40 50 60 70 80 90 100
?
1 2 3 4 5 6 7 8 9 10
Advantages/Disadvantages of Decision Trees
• Advantages:
– Easy to understand (Doctors love them!)
– Easy to generate rules
• Disadvantages:
– May suffer from overfitting.
– Classifies by rectangular partitioning (so does
not handle correlated features very well).
– Can be quite large – pruning is necessary.
– Does not handle streaming data easily
How would we go about building a
classifier for projectile points?
?
length
I. Location of maximum blade width
1. Proximal quarter
2. Secondmost proximal quarter
3. Secondmost distal quarter
4. Distal quarter
II. Base shape
2. Normal curve
3. Triangular
4. Folsomoid
III. Basal indentation ratio
1. No basal indentation
2. 0·90–0·99 (shallow)
3. 0·80–0·89 (deep)
IV. Constriction ratio
1.
2.
3.
4.
5.
6.
1·00
0·90–0·99
0·80–0·89
0·70–0·79
0·60–0·69
0·50–0·59
width
1. Arc-shaped
21225212
V. Outer tang angle
1. 93–115
2. 88–92
3. 81–87
4. 66–88
5. 51–65
6. <50
VI. Tang-tip shape
1. Pointed
2. Round
3. Blunt
VII. Fluting
1. Absent
2. Present
VIII. Length/width ratio
1. 1·00–1·99
2. 2·00–2·99
3. 3.00 -3.99
4. 4·00–4·99
5. 5·00–5·99
6. >6. 6·00
length = 3.10
width = 1.45
length /width ratio= 2.13
I. Location of maximum blade width
1. Proximal quarter
2. Secondmost proximal quarter
3. Secondmost distal quarter
4. Distal quarter
21225212
II. Base shape
1. Arc-shaped
2. Normal curve
3. Triangular
4. Folsomoid
III. Basal indentation ratio
1. No basal indentation
2. 0·90–0·99 (shallow)
3. 0·80–0·89 (deep)
IV. Constriction ratio
1.
2.
3.
4.
5.
6.
1·00
0·90–0·99
0·80–0·89
0·70–0·79
0·60–0·69
0·50–0·59
V. Outer tang angle
1. 93–115
2. 88–92
3. 81–87
4. 66–88
5. 51–65
6. <50
Fluting? = TRUE?
1. Pointed
2. Round
3. Blunt
VII. Fluting
1. Absent
2. Present
VIII. Length/width ratio
1. 1·00–1·99
2. 2·00–2·99
3. 3.00 -3.99
4. 4·00–4·99
5. 5·00–5·99
6. >6. 6·00
yes
no
VI. Tang-tip shape
Base Shape = 4
no
Mississippian
Late Archaic
yes
Length/width ratio = 2
We could also us the Nearest Neighbor Algorithm
?
21225212
21265122 - Late Archaic
14114214 - Transitional Paleo
24225124 - Transitional Paleo
41161212 - Late Archaic
It might be better to use the shape
directly in the decision tree…
Lexiang Ye and Eamonn Keogh (2009) Time
Series Shapelets: A New Primitive for Data
Mining. SIGKDD 2009
Decision Tree for Arrowheads
Clovis
Avonlea
Mix
Training data (subset)
Avonlea
Clovis
(Clovis)
11.24
I
(Avonlea)
85.47
II
Shapelet Dictionary
1.5
1.0
0.5
0
0
100
I
200
300
400
Arrowhead Decision
Tree
II
1
0
2
The shapelet decision tree classifier achieves an
accuracy of 80.0%, the accuracy of rotation
invariant one-nearest-neighbor classifier is 68.0%.
Naïve Bayes Classifier
Thomas Bayes
1702 - 1761
We will start off with a visual intuition, before looking at the math…
Grasshoppers
Katydids
Antenna Length
10
9
8
7
6
5
4
3
2
1
1 2 3 4 5 6 7 8 9 10
Abdomen Length
Remember this example?
Let’s get lots more data…
With a lot of data, we can build a histogram. Let us
just build one for “Antenna Length” for now…
Antenna Length
10
9
8
7
6
5
4
3
2
1
1 2 3 4 5 6 7 8 9 10
Katydids
Grasshoppers
We can leave the
histograms as they are,
or we can summarize
them with two normal
distributions.
Let us us two normal
distributions for ease
of visualization in the
following slides…
• We want to classify an insect we have found. Its antennae are 3 units long.
How can we classify it?
• We can just ask ourselves, give the distributions of antennae lengths we have
seen, is it more probable that our insect is a Grasshopper or a Katydid.
• There is a formal way to discuss the most probable classification…
p(cj | d) = probability of class cj, given that we have observed d
3
Antennae length is 3
p(cj | d) = probability of class cj, given that we have observed d
P(Grasshopper | 3 ) = 10 / (10 + 2)
= 0.833
P(Katydid | 3 )
= 0.166
= 2 / (10 + 2)
10
2
3
Antennae length is 3
p(cj | d) = probability of class cj, given that we have observed d
P(Grasshopper | 7 ) = 3 / (3 + 9)
= 0.250
P(Katydid | 7 )
= 0.750
= 9 / (3 + 9)
9
3
7
Antennae length is 7
p(cj | d) = probability of class cj, given that we have observed d
P(Grasshopper | 5 ) = 6 / (6 + 6)
= 0.500
P(Katydid | 5 )
= 0.500
= 6 / (6 + 6)
66
5
Antennae length is 5
Bayes Classifiers
That was a visual intuition for a simple case of the Bayes classifier,
also called:
• Idiot Bayes
• Naïve Bayes
• Simple Bayes
We are about to see some of the mathematical formalisms, and
more examples, but keep in mind the basic idea.
Find out the probability of the previously unseen instance
belonging to each class, then simply pick the most probable class.
Bayes Classifiers
• Bayesian classifiers use Bayes theorem, which says
p(cj | d ) = p(d | cj ) p(cj)
p(d)
•
p(cj | d) = probability of instance d being in class cj,
This is what we are trying to compute
• p(d | cj) = probability of generating instance d given class cj,
We can imagine that being in class cj, causes you to have feature d
with some probability
• p(cj) = probability of occurrence of class cj,
This is just how frequent the class cj, is in our database
•
p(d) = probability of instance d occurring
This can actually be ignored, since it is the same for all classes
Assume that we have two classes
c1 = male, and c2 = female.
(Note: “Drew
can be a male
or female
name”)
We have a person whose sex we do not
know, say “drew” or d.
Classifying drew as male or female is
equivalent to asking is it more probable
that drew is male or female, I.e which is
greater p(male | drew) or p(female | drew)
Drew Barrymore
Drew Carey
What is the probability of being called
“drew” given that you are a male?
p(male | drew) = p(drew | male ) p(male)
p(drew)
What is the probability
of being a male?
What is the probability of
being named “drew”?
(actually irrelevant, since it is
that same for all classes)
This is Officer Drew (who arrested me in
1997). Is Officer Drew a Male or Female?
Luckily, we have a small
database with names and sex.
We can use it to apply Bayes
rule…
Officer Drew
p(cj | d) = p(d | cj ) p(cj)
p(d)
Name
Drew
Sex
Male
Claudia Female
Drew
Female
Drew
Female
Alberto Male
Female
Karin
Nina
Female
Sergio Male
Name
Sex
Drew
p(cj | d) = p(d | cj ) p(cj)
p(d)
Officer Drew
p(male | drew) = 1/3 * 3/8
3/8
p(female | drew) = 2/5 * 5/8
3/8
Male
Claudia Female
Drew
Female
Drew
Female
Alberto Male
Female
Karin
Nina
Female
Sergio Male
= 0.125
3/8
= 0.250
3/8
Officer Drew is
more likely to be
a Female.
Officer Drew IS a female!
Officer Drew
p(male | drew) = 1/3 * 3/8
3/8
p(female | drew) = 2/5 * 5/8
3/8
= 0.125
3/8
= 0.250
3/8
So far we have only considered Bayes
Classification when we have one
attribute (the “antennae length”, or the
“name”). But we may have many
features.
How do we use all the features?
Name
Drew
Claudia
Drew
Drew
Alberto
Karin
Nina
Sergio
Over 170CM
No
Yes
No
No
Yes
No
Yes
Yes
Eye
Blue
Brown
Blue
Blue
Brown
Blue
Brown
Blue
p(cj | d) = p(d | cj ) p(cj)
p(d)
Hair length
Short
Long
Long
Long
Short
Long
Short
Long
Sex
Male
Female
Female
Female
Male
Female
Female
Male
• To simplify the task, naïve Bayesian classifiers assume
attributes have independent distributions, and thereby estimate
p(d|cj) = p(d1|cj) * p(d2|cj) * ….* p(dn|cj)
The probability of
class cj generating
instance d, equals….
The probability of class cj
generating the observed
value for feature 1,
multiplied by..
The probability of class cj
generating the observed
value for feature 2,
multiplied by..
• To simplify the task, naïve Bayesian classifiers
assume attributes have independent distributions, and
thereby estimate
p(d|cj) = p(d1|cj) * p(d2|cj) * ….* p(dn|cj)
p(officer drew|cj) = p(over_170cm = yes|cj) * p(eye =blue|cj) * ….
Officer Drew
is blue-eyed,
over 170cm
tall, and has
long hair
p(officer drew| Female) = 2/5 * 3/5 * ….
p(officer drew| Male) = 2/3 * 2/3 * ….
The Naive Bayes classifiers
is often represented as this
type of graph…
cj
Note the direction of the
arrows, which state that
each class causes certain
features, with a certain
probability
p(d1|cj)
p(d2|cj)
…
p(dn|cj)
cj
Naïve Bayes is fast and
space efficient
We can look up all the probabilities
with a single scan of the database and
store them in a (small) table…
p(d1|cj)
Sex
Over190cm
Male
Yes
0.15
No
0.85
Yes
0.01
No
0.99
Female
…
p(d2|cj)
Sex
Long Hair
Male
Yes
0.05
No
0.95
Yes
0.70
No
0.30
Female
p(dn|cj)
Sex
Male
Female
Naïve Bayes is NOT sensitive to irrelevant features...
Suppose we are trying to classify a persons sex based on
several features, including eye color. (Of course, eye color
is completely irrelevant to a persons gender)
p(Jessica |cj) = p(eye = brown|cj) * p( wears_dress = yes|cj) * ….
p(Jessica | Female) = 9,000/10,000
p(Jessica | Male) = 9,001/10,000
* 9,975/10,000 * ….
* 2/10,000
* ….
Almost the same!
However, this assumes that we have good enough estimates of
the probabilities, so the more data the better.
cj
An obvious point. I have used a
simple two class problem, and
two possible values for each
example, for my previous
examples. However we can have
an arbitrary number of classes, or
feature values
p(d1|cj)
Animal
Mass >10kg
Cat
Yes
0.15
No
Dog
Pig
p(d2|cj)
…
Animal
Animal
Color
Cat
Black
0.33
0.85
White
0.23
Yes
0.91
Brown
0.44
No
0.09
Black
0.97
Yes
0.99
White
0.03
No
0.01
Brown
0.90
Black
0.04
White
0.01
Dog
Pig
p(dn|cj)
Cat
Dog
Pig
Problem!
p(d|cj)
Naïve Bayesian
Classifier
Naïve Bayes assumes
independence of
features…
p(d1|cj)
Sex
Over 6
foot
Male
Yes
0.15
No
0.85
Yes
0.01
No
0.99
Female
p(d2|cj)
Sex
Over 200
pounds
Male
Yes
0.11
No
0.80
Yes
0.05
No
0.95
Female
p(dn|cj)
Solution
p(d|cj)
Naïve Bayesian
Classifier
Consider the
relationships between
attributes…
p(d1|cj)
Sex
Male
Female
Over 6
foot
Yes
0.15
No
0.85
Yes
0.01
No
0.99
p(d2|cj)
p(dn|cj)
Sex
Over 200 pounds
Male
Yes and Over 6 foot
0.11
No and Over 6 foot
0.59
Yes and NOT Over 6 foot
0.05
No and NOT Over 6 foot
0.35
Solution
p(d|cj)
Naïve Bayesian
Classifier
Consider the
relationships between
attributes…
p(d1|cj)
p(d2|cj)
But how do we find the set of connecting arcs??
p(dn|cj)
The Naïve Bayesian Classifier has a piecewise quadratic decision boundary
Katydids
Grasshoppers
Ants
Adapted from slide by Ricardo Gutierrez-Osuna
Which of the “Pigeon Problems” can be
solved by a decision tree?
10
9
8
7
6
5
4
3
2
1
1 2 3 4 5 6 7 8 9 10
10
9
8
7
6
5
4
3
2
1
100
90
80
70
60
50
40
30
20
10
10 20 30 40 50 60 70 80 90 100
1 2 3 4 5 6 7 8 9 10
Advantages/Disadvantages of Naïve Bayes
• Advantages:
–
–
–
–
Fast to train (single scan). Fast to classify
Not sensitive to irrelevant features
Handles real and discrete data
Handles streaming data well
• Disadvantages:
– Assumes independence of features
Summary
• We have seen the four most common algorithms used for classification.
• We have seen there is no one “best” algorithm.
• We have seen that issues like normalizing, cleaning, converting the data can make
a huge difference.
• We have only scratched the surface!
• How do we learn with no class labels? (clustering)
• How do we learn with expensive class labels? (active learning)
• How do we spot outliers (Anomaly detection)
• How do we…..
Popular Science Book
The Master Algorithm
by Pedro Domingos
Textbook
Data Mining:
by Charu C. Aggarwal
Malaria
Malaria afflicts about
4% of all humans,
killing one million of
them each year.
Malaria Deaths (2003)
www.worldmapper.org
There are interventions to mitigate the problem
A recent meta-review of randomized controlled trials
of Insecticide Treated Nets (ITNs) found that ITNs
can reduce malaria-related deaths in children by one
fifth and episodes of malaria by half.
Mosquito nets work!
How do we know where to do the
interventions, given that we have
finite resources?
One second of audio from our sensor.
The Common Eastern Bumble Bee
(Bombus impatiens) takes about one tenth
of a second to pass the laser.
0.2
0.1
0
Background noise
-0.1
Bee begins to cross laser
Bee has past though the laser
-0.2
0
0.5
1
1.5
2
2.5
3
3.5
4
x 10 4
4.5
http://www.youtube.com/watch?v=WT0UAGRH0T8
One second of audio from the laser sensor. Only
Bombus impatiens (Common Eastern Bumble
Bee) is in the insectary.
0.2
0.1
0
Background noise
-0.1
Bee begins to cross laser
-0.2
0
0.5
1
1.5
2
2.5
-3
x 10
|Y(f)|
4
Peak at
197Hz
60Hz
interference
2
3.5
Single-Sided Amplitude Spectrum of Y(t)
4
3
3
4
x 10
4.5
Harmonics
1
0
0
100
200
300
400
500
600
Frequency (Hz)
700
800
900
1000
-3
x 10
|Y(f)|
4
3
2
1
0
0
100
200
300
400
500
600
700
800
900
1000
700
800
900
1000
700
800
900
1000
Frequency (Hz)
0
100
200
300
400
500
600
Frequency (Hz)
0
100
200
300
400
500
600
Frequency (Hz)
-3
x 10
|Y(f)|
4
3
2
1
0
0
100
200
300
400
500
600
700
800
900
1000
700
800
900
1000
700
800
900
1000
Frequency (Hz)
0
100
200
300
400
500
600
Frequency (Hz)
0
100
200
300
400
500
600
Frequency (Hz)
0
100
200
300
400
Wing Beat Frequency Hz
500
600
700
Anopheles stephensi is a
primary mosquito vector
of malaria.
0
100
200
The yellow fever mosquito
(Aedes aegypti) is a mosquito
that can spread dengue fever,
chikungunya, and yellow fever
viruses
300
400
Wing Beat Frequency Hz
500
600
700
400
Anopheles stephensi: Female
mean =475, Std = 30
500
600
517
700
Aedes aegyptii : Female
mean =567, Std = 43
If I see an insect with a wingbeat frequency of 500, what is it?
𝑃 𝐴𝑛𝑜𝑝ℎ𝑒𝑙𝑒𝑠 𝑤𝑖𝑛𝑔𝑏𝑒𝑎𝑡 = 500 =
1
2𝜋 30
(500−475)2
−
2×30 2
𝑒
517
400
500
What is the error rate?
Can we get more features?
600
12.2% of the
area under the
pink curve
700
8.02% of the
area under the
red curve
Circadian Features
Aedes aegypti (yellow fever mosquito)
0
0
Midnight
dusk
dawn
12
Noon
24
Midnight
70
600
500
Suppose I observe an
insect with a wingbeat
frequency of 420Hz
400
What is it?
70
600
500
Suppose I observe an
insect with a wingbeat
frequency of 420Hz at
11:00am
400
What is it?
0
Midnight
12
Noon
24
Midnight
70
600
500
Suppose I observe an
insect with a wingbeat
frequency of 420 at
11:00am
400
What is it?
0
Midnight
(Culex | [420Hz,11:00am])
12
Noon
24
Midnight
= (6/ (6 + 6 + 0)) * (2/ (2 + 4 + 3)) = 0.111
(Anopheles | [420Hz,11:00am]) = (6/ (6 + 6 + 0)) * (4/ (2 + 4 + 3)) = 0.222
(Aedes | [420Hz,11:00am])
= (0/ (6 + 6 + 0)) * (3/ (2 + 4 + 3)) = 0.000
Blue Sky Ideas
Once you have a classifier working, you begin
to see new uses for them…
Let us see some examples..
Capturing or killing individually targeted insects
• Most efforts to capture or kill insects are “shotgun”. Many nontargeted insects (including beneficial ones) are killed/captured.
• In some cases, the ratios are 1,000 to 1 (i.e. 1,000 non-targeted
insects are effected for each one that was targeted).
• We believe our sensors allow an ultra precise approach, with a ratio
approaching 1 to 1.
• This has obvious implications for SIT/metagenomics
Kill
It seems obvious you could kill a
mosquito with a powerful enough
laser and with enough time.
But we need to do it fast, with as little
power as possible.
We have gotten this down to 1/20th of
a second, and just 1 watt. (and falling)
The mosquitoes may survive the laser
strike, but they cannot fly away (as
was the case in photo shown right)
We are building a SIT Hotel
California for female
mosquitoes (you can check
out anytime you like, but
you can never leave)
Collaboration with UCR
mechanical engineers Amir Rose
and Dr. Guillermo Aguilar
Culex tarsalis
Zoom-in (after removing the wing)
Capture
We envision building robotic traps that can be left in the field, and
programed with different sampling missions.
Such traps could be placed and retrieved by drones.
Capturing live insects is important if you want to do metagenomics.
Some examples of sampling missions…

Capture examples of gravid{Aedes aegypti}


Capture insects marked{ Cripple(left-C|right-S) }
Capture examples of insects that are NOT Anopheles AND have a wingbeat frequency > 400


Capture examples of any insects with a wingbeat frequency > 500, encountered between 4:00am and 4:10am
Capture examples of fed{Anopheles gambiae} OR fed{Anopheles quadriannulatus} OR fed{Anopheles melas}
(to exclude bees, etc.)
Capture
About 10% of the insects captured
by Venus fly traps are flying insects
We believe that we can build
inexpensive mechanical traps that
can capture sex/species targeted
insects.

Capture examples of gravid{Aedes aegypti}


Capture insects marked{ Cripple(left-C|right-S) }
Capture examples of insects that are NOT Anopheles AND have a wingbeat frequency > 400


Capture examples of any insects with a wingbeat frequency > 500, encountered between 4:00am and 4:10am
Capture examples of fed{Anopheles gambiae} OR fed{Anopheles quadriannulatus} OR fed{Anopheles melas}
(to exclude bees, etc.)
Classification Problem: Fourth Amendment Cases before the Supreme Court II
The Supreme Court’s search and seizure decisions, 1962–1984 terms.
Keogh vs. State of California = {0,1,1,0,0,0,1,0}
U = Unreasonable
R = Reasonable
We can also learn decision trees for
individual Supreme Court Members.
Using similar decision trees for the
other eight justices, these models
correctly predicted the majority
opinion in 75 percent of the cases,
substantially outperforming the
experts' 59 percent.
Decision Tree for Supreme Court
Justice Sandra Day O'Connor