1. Let see just one example….
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
Antennae
Insect Class 1
2.7
5.5
Grasshopper 2
8.0
9.1
Katydid 3
0.9
4.7 4
1.1
3.1 5
5.4
8.5 6
2.9
1.9 7
6.1
6.6 8
0.5
1.0 9
8.3 10
8.1
Katydids

2. (Western Pipistrelle
(Parastrellus hesperus)
Photo by Michael Durham

3. A spectrogram of a bat call.
Western pipistrelle calls

4. Characteristic frequency
We can easily measure two features of bat calls. Their characteristic frequency and their call duration
Characteristic frequency
Call duration
Bat ID
Characteristic frequency
Call duration (ms)
Bat Species 1
49.7
5.5
Western pipistrelle

9. 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..

10. I have a box of apples.. 1 H(X) Pr(X = good) = p
then Pr(X = bad) = 1 − p 
the entropy of X is given by
0.5
binary entropy function attains its maximum value when p = 0.5 1
All good
All bad

11. Decision Tree Classifier10 1 2 3 4 5 6 7 8 9
Ross Quinlan
Abdomen Length > 7.1?
Antenna Length no
yes
Antenna Length > 6.0?
Katydid no
yes
Grasshopper
Katydid
Abdomen Length

12. Antennae shorter than body?
Yes No
3 Tarsi?
Grasshopper
Yes No
Foretiba has ears?
Yes No
Cricket
Decision trees predate computers
Katydids
Camel Cricket

13. Decision Tree Classification
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

14. 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

15. 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
0 log(0) is defined as 0

16. 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
Note: entropy is at its minimum if the collection of objects is completely uniform

17. Person Homer 0” 250 36 M Marge 10” 150 34 F Bart 2” 90 10 Lisa 6” 78 8
Hair Length
Weight
Age
Class
Homer 0”
250 36 M
Marge
10”
150 34 F
Bart 2” 90 10
Lisa 6” 78 8
Maggie 4” 20 1
Abe 1”
170 70
Selma 8”
160 41
Otto
180 38
Krusty
200 45
Comic 8”
290 38 ?

18. Let us try splitting on Hair length
Entropy(4F,5M) = -(4/9)log2(4/9) - (5/9)log2(5/9) =
yes no
Hair Length <= 5?
Let us try splitting on Hair length
Entropy(1F,3M) = -(1/4)log2(1/4) - (3/4)log2(3/4) =
Entropy(3F,2M) = -(3/5)log2(3/5) - (2/5)log2(2/5) =
Gain(Hair Length <= 5) = – (4/9 * /9 * ) =

19. Let us try splitting on Weight
Entropy(4F,5M) = -(4/9)log2(4/9) - (5/9)log2(5/9) =
yes no
Weight <= 160?
Let us try splitting on Weight
Entropy(4F,1M) = -(4/5)log2(4/5) - (1/5)log2(1/5) =
Entropy(0F,4M) = -(0/4)log2(0/4) - (4/4)log2(4/4)
= 0
Gain(Weight <= 160) = – (5/9 * /9 * 0 ) =

20. Let us try splitting on Age
Entropy(4F,5M) = -(4/9)log2(4/9) - (5/9)log2(5/9) =
yes no
age <= 40?
Let us try splitting on Age
Entropy(3F,3M) = -(3/6)log2(3/6) - (3/6)log2(3/6)
= 1
Entropy(1F,2M) = -(1/3)log2(1/3) - (2/3)log2(2/3) =
Gain(Age <= 40) = – (6/9 * 1 + 3/9 * ) =

21. This time we find that we can split on Hair length, and we are done!
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!
yes no
Weight <= 160?
This time we find that we can split on Hair length, and we are done!
yes no
Hair Length <= 2?

22. We need don’t need to keep the data around, just the test conditions.
Weight <= 160?
yes no
How would these people be classified?
Hair Length <= 2?
Male
yes no
Male
Female

23. Male Male Female It is trivial to convert Decision Trees to rules… yes
Weight <= 160?
yes no
Hair Length <= 2?
Male
yes no
Male
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

24. 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.

25. PSA = serum prostate-specific antigen levels PSAD = PSA density
TRUS = transrectal ultrasound 
Garzotto M et al. JCO 2005;23:

26. The worked examples we have seen were performed on small datasets
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”…

27. 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”

28. ? Which of the “Pigeon Problems” can be solved by a Decision Tree?10 1 2 3 4 5 6 7 8 9
Deep Bushy Tree
Useless ? 10 1 2 3 4 5 6 7 8 9
100 10 20 30 40 50 60 70 80 90
The Decision Tree has a hard time with correlated attributes

29. Advantages/Disadvantages of Decision Trees
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

31. How would we go about building a classifier for projectile points?

32. 21225212 length width length = 3.10 width = 1.45
I. Location of maximum blade width
1. Proximal quarter
2. Secondmost proximal quarter
3. Secondmost distal quarter
4. Distal quarter
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
1·00
0·90–0·99
0·80–0·89
·70–0·79
·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
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
4. 4·00–4·99
5. 5·00–5·99
6. >6. 6·00
width
length = 3.10
width = 1.45
length /width ratio= 2.13

33. 21225212 yes no no yes Fluting? = TRUE? Base Shape = 4
I. Location of maximum blade width
1. Proximal quarter
2. Secondmost proximal quarter
3. Secondmost distal quarter
4. Distal quarter
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
1·00
0·90–0·99
0·80–0·89
·70–0·79
·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
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
4. 4·00–4·99
5. 5·00–5·99
6. >6. 6·00
Fluting? = TRUE? no
yes
Base Shape = 4
Late Archaic no
yes
Mississippian
Length/width ratio = 2

34. ? 21225212 21265122 - Late Archaic 14114214 - Transitional Paleo
We could also us the Nearest Neighbor Algorithm ?
Late Archaic
Transitional Paleo
Transitional Paleo
Late Archaic
Woodland

35. Arrowhead Decision Tree
Decision Tree for Arrowheads
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
Avonlea
Clovis
Mix
Training data (subset)
11.24
85.47
Shapelet Dictionary
(Clovis)
(Avonlea) I II
100
200
300
400
0.5
1.0
1.5
Arrowhead Decision Tree 2 1
Clovis
Avonlea
The shapelet decision tree classifier achieves an accuracy of 80.0%, the accuracy of rotation invariant one-nearest-neighbor classifier is 68.0%.

36. Naïve Bayes Classifier
Thomas Bayes
We will start off with a visual intuition, before looking at the math…

37. Grasshoppers Katydids Remember this example? Let’s get lots more data…10 1 2 3 4 5 6 7 8 9
Antenna Length
Abdomen Length
Remember this example? Let’s get lots more data…

38. With a lot of data, we can build a histogram
With a lot of data, we can build a histogram. Let us just build one for “Antenna Length” for now… 10 1 2 3 4 5 6 7 8 9
Antenna Length
Katydids
Grasshoppers

39. 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…

40. p(cj | d) = probability of class cj, given that we have observed d
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

41. p(cj | d) = probability of class cj, given that we have observed d
P(Grasshopper | 3 ) = 10 / (10 + 2) = 0.833
P(Katydid | 3 ) = 2 / (10 + 2) = 0.166 10 2 3
Antennae length is 3

42. p(cj | d) = probability of class cj, given that we have observed d
P(Grasshopper | 7 ) = 3 / (3 + 9) = 0.250
P(Katydid | 7 ) = 9 / (3 + 9) = 0.750 9 3 7
Antennae length is 7

43. p(cj | d) = probability of class cj, given that we have observed d
P(Grasshopper | 5 ) = 6 / (6 + 6) = 0.500
P(Katydid | 5 ) = 6 / (6 + 6) = 0.500 6 6 5
Antennae length is 5

44. 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.

45. 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

46. c1 = male, and c2 = female. Assume that we have two classes
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)
(Note: “Drew can be a male or female name”)
Drew Barrymore
Drew Carey
What is the probability of being called “drew” given that you are a male?
What is the probability of being a male?
p(male | drew) = p(drew | male ) p(male)
p(drew)
What is the probability of being named “drew”? (actually irrelevant, since it is that same for all classes)

47. p(cj | d) = p(d | cj ) p(cj) p(d)
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…
Name
Sex
Drew
Male
Claudia
Female
Alberto
Karin
Nina
Sergio
Officer Drew
p(cj | d) = p(d | cj ) p(cj) p(d)

48. p(cj | d) = p(d | cj ) p(cj) p(d)
Name
Sex
Drew
Male
Claudia
Female
Alberto
Karin
Nina
Sergio
p(cj | d) = p(d | cj ) p(cj) p(d)
Officer Drew
p(male | drew) = 1/3 * 3/8 = 0.125
3/ /8
Officer Drew is more likely to be a Female.
p(female | drew) = 2/5 * 5/8 = 0.250
3/ /8

49. Officer Drew IS a female!
p(male | drew) = 1/3 * 3/8 = 0.125
3/ /8
p(female | drew) = 2/5 * 5/8 = 0.250
3/ /8

50. p(cj | d) = p(d | cj ) p(cj) p(d)
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?
p(cj | d) = p(d | cj ) p(cj) p(d)
Name
Over 170CM
Eye
Hair length
Sex
Drew No
Blue
Short
Male
Claudia
Yes
Brown
Long
Female
Alberto
Karin
Nina
Sergio

51. p(d|cj) = p(d1|cj) * p(d2|cj) * ….* p(dn|cj)
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..

52. p(d|cj) = p(d1|cj) * p(d2|cj) * ….* p(dn|cj)
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 * ….

53. cj … p(d1|cj) p(d2|cj) p(dn|cj)
The Naive Bayes classifiers is often represented as this type of graph…
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)

54. 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) p(d2|cj) p(dn|cj)
Sex
Over190cm
Male
Yes
0.15 No
0.85
Female
0.01
0.99
Sex
Long Hair
Male
Yes
0.05 No
0.95
Female
0.70
0.30
Sex
Male
Female

55. 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, * 9,975/10,000 * ….
p(Jessica | Male) = 9,001/10, * 2/10, * ….
Almost the same!
However, this assumes that we have good enough estimates of the probabilities, so the more data the better.

56. 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) p(d2|cj) p(dn|cj)
Animal
Mass >10kg
Cat
Yes
0.15 No
0.85
Dog
0.91
0.09
Pig
0.99
0.01
Animal
Color
Cat
Black
0.33
White
0.23
Brown
0.44
Dog
0.97
0.03
0.90
Pig
0.04
0.01
0.95
Animal
Cat
Dog
Pig

57. Naïve Bayesian Classifier
Problem!
Naïve Bayes assumes independence of features…
p(d|cj)
Naïve Bayesian Classifier
p(d1|cj) p(d2|cj) p(dn|cj)
Sex
Over 6 foot
Male
Yes
0.15 No
0.85
Female
0.01
0.99
Sex
Over 200 pounds
Male
Yes
0.11 No
0.80
Female
0.05
0.95

58. Naïve Bayesian Classifier
Solution
Consider the relationships between attributes…
p(d|cj)
Naïve Bayesian Classifier
p(d1|cj) p(d2|cj) p(dn|cj)
Sex
Over 6 foot
Male
Yes
0.15 No
0.85
Female
0.01
0.99
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
Female
0.01

59. Naïve Bayesian Classifier
Solution
Consider the relationships between attributes…
p(d|cj)
Naïve Bayesian Classifier
p(d1|cj) p(d2|cj) p(dn|cj)
But how do we find the set of connecting arcs??

60. The Naïve Bayesian Classifier has a piecewise quadratic decision boundary
Grasshoppers
Katydids
Ants
Adapted from slide by Ricardo Gutierrez-Osuna

61. Which of the “Pigeon Problems” can be solved by a decision tree?10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9
100 10 20 30 40 50 60 70 80 90

62. Advantages/Disadvantages of Naïve Bayes
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

63. 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

65. Malaria
Malaria afflicts about 4% of all humans, killing one million of them each year.

67. Malaria Deaths (2003)

68. 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!

69. How do we know where to do the interventions, given that we have finite resources?

70. 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
-0.1
Background noise
Bee begins to cross laser
Bee has past though the laser
-0.2
x 10 4
0.5 1
1.5 2
2.5 3
3.5 4
4.5

72. Bee begins to cross laser
One second of audio from the laser sensor. Only Bombus impatiens (Common Eastern Bumble Bee) is in the insectary.
0.2
0.1
-0.1
Background noise
Bee begins to cross laser
-0.2 4
x 10
0.5 1
1.5 2
2.5 3
3.5 4
4.5 -3
x 10
Single-Sided Amplitude Spectrum of Y(t) 4
Peak at 197Hz 3
60Hz interference
|Y(f)| 2
Harmonics 1
100
200
300
400
500
600
700
800
900
1000
Frequency (Hz)

73. 4 3 |Y(f)| 2 1 Frequency (Hz) Frequency (Hz) Frequency (Hz) x 10 100-3
x 10 4 3
|Y(f)| 2 1
100
200
300
400
500
600
700
800
900
1000
Frequency (Hz)
100
200
300
400
500
600
700
800
900
1000
Frequency (Hz)
100
200
300
400
500
600
700
800
900
1000
Frequency (Hz)

74. 4 3 |Y(f)| 2 1 Frequency (Hz) Frequency (Hz) Frequency (Hz) x 10 100-3
x 10 4 3
|Y(f)| 2 1
100
200
300
400
500
600
700
800
900
1000
Frequency (Hz)
100
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400
500
600
700
800
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1000
Frequency (Hz)
100
200
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400
500
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800
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1000
Frequency (Hz)

75. 100
200
300
400
500
600
700
Wing Beat Frequency Hz

76. Anopheles stephensi is a primary mosquito vector of malaria.
The yellow fever mosquito (Aedes aegypti) is a mosquito that can spread dengue fever, chikungunya, and yellow fever viruses
100
200
300
400
500
600
700
Wing Beat Frequency Hz

77. Anopheles stephensi: Female mean =475, Std = 30
400
500
600
700
Anopheles stephensi: Female
mean =475, Std = 30
Aedes aegyptii : Female
mean =567, Std = 43
517
If I see an insect with a wingbeat frequency of 500, what is it?

78. Can we get more features?
517
400
500
600
700
8.02% of the area under the red curve
What is the error rate?
12.2% of the area under the pink curve
Can we get more features?

79. Aedes aegypti (yellow fever mosquito)
Circadian Features
Aedes aegypti (yellow fever mosquito)
dawn
dusk 12 24
Midnight
Noon
Midnight

80. Suppose I observe an insect with a wingbeat frequency of 420Hz
700
Suppose I observe an insect with a wingbeat frequency of 420Hz
What is it?
600
500
400

81. 400
500
600
700
Midnight 12 24
Noon
Suppose I observe an insect with a wingbeat frequency of 420Hz at 11:00am
What is it?

82. 400
500
600
700
Midnight 12 24
Noon
Suppose I observe an insect with a wingbeat frequency of 420 at 11:00am
What is it?
(Culex | [420Hz,11:00am]) = (6/ ( )) * (2/ ( )) = 0.111
(Anopheles | [420Hz,11:00am]) = (6/ ( )) * (4/ ( )) = 0.222
(Aedes | [420Hz,11:00am]) = (0/ ( )) * (3/ ( )) = 0.000

83. Blue Sky Ideas
Once you have a classifier working, you begin to see new uses for them…
Let us see some examples..

84. Capturing or killing individually targeted insects
Most efforts to capture or kill insects are “shotgun”. Many non- targeted 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

85. Zoom-in (after removing the wing)
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)
Culex tarsalis
Collaboration with UCR mechanical engineers Amir Rose and Dr. Guillermo Aguilar
Zoom-in (after removing the wing)

86. 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 > (to exclude bees, etc.)
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}

87. 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 > (to exclude bees, etc.)
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}

88. Keogh vs. State of California = {0,1,1,0,0,0,1,0}
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

89. Decision Tree for Supreme Court Justice Sandra Day O'Connor
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