Instance based and Bayesian
learning
Kurt Driessens
with slide ideas from
a.o
.
Hendrik
Blockeel
, Pedro
Domingos
, David Page,
Tom
Dietterich
and
Eamon
Keogh
Overview
Nearest neighbor methods
–
Similarity
–
Problems:
•
dimensionality of data, efficiency, etc.
–
Solutions:
•
weighting, edited NN, kD

trees, etc.
Naïve Bayes
–
Including an introduction to Bayesian ML methods
Nearest Neighbor: A very simple idea
Imagine the world’s
music collection
represented in
some space
When you like a song,
other songs residing
close to it should
also be interesting
…
Picture from Oracle
Nearest Neighbor Algorithm
1.
Store all the examples <
x
i
,y
i
>
2.
Classify a new example
x
by finding the
stored example
x
k
that most resembles it and
predicts that example’s class y
k
+
+
+
+
+
+
+
+
+
+
+













?
Some properties
•
Learning is very fast
(although we come back to this later)
•
No information is lost
•
Hypothesis space
–
variable size
–
complexity of the hypothesis rises with the
number of stored examples
Decision Boundaries
+
+
+
+
+
+
+
+









Voronoi diagram
Boundaries
are not
computed!
Keeping All Information
Advantage
: no details lost
Disadvantage
: "details" may be noise
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+














+
+
k

Nearest

Neighbor: kNN
To improve robustness against noisy learning
examples, use a set of nearest neighbors
For classification:
use voting
k

Nearest

Neighbor: kNN (2)
For regression:
use the mean
1
1
1
2
2
4
4
4
4
3
3
3
5
5
5
Lazy vs Eager Learning
kNN doesn’t do anything until it needs to make a
prediction =
lazy learner
–
Learning is fast!
–
Predictions require work and can be slow
Eager learners
start computing as soon as they
receive data
Decision tree algorithms, neural networks, …
–
Learning can be slow
–
Predictions are usually fast!
Similarity measures
Distance metrics: measure of dis

similarity
E.g. Manhattan, Euclidean or L
n

norm for numerical
attributes
Hamming distance for nominal attributes
d
(
x
,
y
)
(
x
i
,
y
i
)
i
1
n
whe
r
e
(
x
i
,
y
i
)
0
i
f
x
i
y
i
(
x
i
,
y
i
)
1
i
f
x
i
y
i
Distance definition = critical!
E.g. comparing humans
1.
1.85m, 37yrs
2.
1.83m, 35yrs
3.
1.65m, 37yrs
d(1,2) = 2.00
…
0999975
…
d(1,3) = 0.2
d(2,3) = 2.00808
…
1.
185cm, 37yrs
2.
183cm, 35yrs
3.
165cm, 37yrs
d(1,2) = 2.8284
…
d(1,3) = 20.0997
…
d(2,3) = 18.1107
…
Normalize attribute values
Rescale all dimensions such that the range is
equal, e.g. [

1,1] or [0,1]
For [0,1] range:
with m
i
the minimum and M
i
the maximum value for attribute i
x
i
'
x
i
m
i
M
i
m
i
Curse of dimensionality
Assume a uniformly distributed set of 5000
examples
To capture 5 nearest neighbors we need:
–
in 1 dim: 0.1% of the range
–
in 2 dim: = 3.1% of the range
–
in n dim: 0.1%
1/n
0.1%
Curse of Dimensionality (2)
With 5000 points in 10 dimensions, each
attribute range must be covered approx. 50%
to find 5 neighbors …
?
Curse of Noisy Features
Irrelevant features destroy the metric’s
meaningfulness
Consider a 1dim problem where the query x is at the origin,
the nearest neighbor x
1
is at 0.1 and the second neighbor
x
2
at 0.5 (after normalization)
Now add a uniformly
random feature. What is
the probability that x
2
becomes the closest
neighbor?
approx. 15% !!
Curse of Noisy Features (2)
Location of x
1
vs x
2
on informative dimension
Weighted Distances
Solution: Give each attribute a different weight
in the distance computation
for each
attribute
for
each
class
for each
example in
that class
Selecting attribute weights
Several options:
–
Experimentally find out which weights work well
(cross

validation)
–
Other solutions
,
e.g. (
Langley,1996)
1.
Normalize attributes (to scale 0

1)
2.
Then select weights according to "average attribute
similarity within class”
More distances
Strings
–
Levenshtein
distance/edit distance
=
minimal number of changes
needed to change
one word into the other
Allowed edits/changes:
1.
delete character
2.
insert character
3.
change character
(not used by some other edit

distances)
Even more distances
n
i
i
i
c
q
C
Q
D
1
2
,
Q
C
D
(
Q
,
C
)
Given two time series:
Q
=
q
1
…
q
n
C
=
c
1
…
c
n
Euclidean
Start and end times are critical!
D
(
Q
,
R
)
R
Sequence distances (2)
Dynamic Time Warping
Dimensionality reduction
Fixed Time Axis
Sequences are aligned “one to one”.
“
Warped
”
Time Axis
Nonlinear alignments are possible.
Distance

weighted kNN
k places arbitrary border on example relevance
–
Idea: give higher weight to closer instances
Can now use all training instances instead of only k
(“Shepard’s method”)
2
1
1
)
,
(
1
with
)
(
)
(
ˆ
i
q
i
k
i
i
k
i
i
i
q
x
x
d
w
w
x
f
w
x
f
!
In high

dimensional spaces, a function of d that “goes to zero fast
enough” is needed.
(Again “curse of dimensionality”.)
Fast Learning
–
Slow Predictions
Efficiency
–
For each prediction, kNN needs to compute the
distance (i.e. compare all attributes) for ALL stored
examples
–
Prediction time = linear in the size of the data

set
For large training sets and/or complex distances, this
can be too slow to be practical
(1) Edited k

nearest neighbor
Use only part of the training data
✔
Less storage
✗
Order dependent
✗
Sensitive to noisy data
More advanced
alternatives exist (= IB3)
(2) Pipeline filters
Reduce time spent on far

away examples by
using more efficient distance

estimates first
–
Eliminate most examples using rough distance
approximations
–
Compute more precise distances for examples in
the neighborhood
(3) kD

trees
Use a clever data

structure to eliminate the
need to compute all distances
kD

trees are similar to decision trees except
–
splits are made on the median/mean value of
dimension with highest variance
–
each node stores one data point, leaves can be
empty
Example kD

tree
Use a form of A* search using the minimum distance to a
node as an underestimate of the true closest distance
Finds closest neighbor in logarithmic (depth of tree) time
kD

trees (cont.)
Building a good kD

tree may take some time
–
Learning time is no longer 0
–
Incremental learning is no longer trivial
•
kD

tree will no longer be balanced
•
re

building the tree is recommended when the max

depth becomes larger than 2* the minimal required
depth (= log(N) with N training examples)
Cover trees
are more advanced, more complex,
and more efficient!!
(4) Using
Prototypes
The rough decision surfaces of nearest neighbor
can sometimes be considered a disadvantage
–
Solve two problems at once by using prototypes
= Representative for a whole group of instances
+
+
+
+



+
+
+
+



+

Prototypes (cont.)
Prototypes can be:
–
Single instance, replacing a group
–
Other structure (e.g., rectangle, rule, ...)

> in this case: need to define distance
+
+
+
+



Recommender Systems through
instance based learning
Movie
Alice (1)
Bob (2)
Carol (3)
Dave (4)
(romance)
(action)
Love at last
5
5
0
0
0.9
0
Romance forever
5
?
?
0
1.0
0.01
Cute puppies of love
?
4
0
?
0.99
0
Nonstop car chases
0
0
5
4
0.1
1.0
Swords vs. karate
0
0
5
?
0
0.9
Predict ratings for films users have not yet seen (or rated).
Recommender Systems
Predict through instance based regression:
Some Comments on
k

NN
Positive
•
Easy to implement
•
Good “baseline” algorithm /
experimental control
•
Incremental learning easy
•
Psychologically plausible
model of human memory
Negative
•
Led astray by irrelevant
features
•
No insight into domain (no
explicit model)
•
Choice of distance function
is problematic
•
Doesn’t exploit/notice
structure in examples
Summary
•
Generalities of instance based learning
–
Basic idea, (dis)advantages, Voronoi diagrams, lazy
vs. eager learning
•
Various instantiations
–
kNN, distance

weighted methods, ...
–
Rescaling attributes
–
Use of prototypes
Bayesian learning
This is going to be very introductory
•
Describing (results of) learning processes
–
MAP and ML hypotheses
•
Developing practical learning algorithms
–
Naïve Bayes learner
•
application: learning to classify texts
–
Learning Bayesian belief networks
Bayesian approaches
Several roles for probability theory in machine
learning:
–
describing existing learners
•
e.g. compare them with “optimal” probabilistic
learner
–
developing practical learning algorithms
•
e.g. “Naïve Bayes” learner
Bayes’ theorem
plays a central role
Basics of probability
•
P(A): probability that A happens
•
P(AB): probability that A happens, given that
B happens (“conditional probability”)
•
Some rules:
–
complement: P(not A) = 1

P(A)
–
disjunction: P(A or B) = P(A)+P(B)

P(A and B)
–
conjunction: P(A and B) = P(A) P(BA)
= P(A) P(B) if A and B independent
–
total probability:P(A) =
i
P(AB
i
) P(B
i
)
With each B
i
mutually exclusive
Bayes’ Theorem
P(AB) = P(BA) P(A) / P(B)
Mainly 2 ways of using Bayes’ theorem:
–
Applied to learning a hypothesis h from data D:
P(hD) = P(Dh) P(h) / P(D) ~ P(Dh)P(h)
–
P(h): a priori probability that h is correct
–
P(hD): a posteriori probability that h is correct
–
P(D): probability of obtaining data D
–
P(Dh): probability of obtaining data D if h is correct
–
Applied to classification of a single example e:
P(classe) = P(eclass)P(class)/P(e)
Bayes’ theorem: Example
Example:
–
assume some lab test for a disease has 98%
chance of giving positive result if disease is
present, and 97% chance of giving negative result
if disease is absent
–
assume furthermore 0.8% of population has this
disease
–
given a positive result, what is the probability that
the disease is present?
P(
DisPos
) = P(
PosDis
)P(Dis) / P(
Pos
) =
0.98*0.008 / (0.98*0.008 + 0.03*0.992)
MAP and ML hypotheses
Task:
Given the current data D and some
hypothesis space H, return the hypothesis h in H
that is most likely to be correct
.
Note: this h is
optimal
in a certain sense
–
no method can exist that finds with higher
probability the correct h
MAP hypothesis
Given some data D and a hypothesis space H,
find the hypothesis
h
H
that has the highest
probability of being correct; i.e., P(
hD
) is
maximal
This hypothesis is called the
maximal a posteriori
hypothesis
h
MAP
:
h
MAP
=
argmax
h
H
P(
hD
)
=
argmax
h
H
P(
Dh
)P(h)/P(D) =
argmax
h
H
P(
Dh
)P(h)
•
last equality holds because P(D) is constant
So : we need P(
Dh
) and P(h) for all
h
H
to compute
h
MAP
ML hypothesis
P(h): a priori probability that h is correct
What if no preferences for one h over another?
•
Then assume P(h) = P(h’) for all h, h’
H
•
Under this assumption h
MAP
is called the
maximum
likelihood hypothesis
h
ML
h
ML
= argmax
h
H
P(Dh)
(because P(h) constant)
•
How to find h
MAP
or h
ML
?
–
brute force method: compute P(Dh), P(h) for all h
H
–
usually not feasible
Naïve Bayes classifier
Simple & popular classification method
•
Based on Bayes’ rule + assumption of
conditional independence
–
assumption often violated in practice
–
even then, it usually works well
Example application: classification of text
documents
Classification using Bayes rule
Given attribute values, what is most probable value of
target variable?
Problem: too much data needed to estimate P(a
1
…a
n
v
j
)
)
(
)

,...,
,
(
max
arg
)
,...,
,
(
)
(
)

,...,
,
(
max
arg
)
,...,
,

(
max
arg
2
1
2
1
2
1
2
1
j
j
n
V
v
n
j
j
n
V
v
n
j
V
v
MAP
v
P
v
a
a
a
P
a
a
a
P
v
P
v
a
a
a
P
a
a
a
v
P
v
j
j
j
The Naïve Bayes classifier
Naïve Bayes assumption
: attributes are
independent, given the class
P(a
1
,…,a
n
v
j
) = P(a
1
v
j
)P(a
2
v
j
)…P(a
n
v
j
)
–
also called
conditional independence
(given the
class)
•
Under that assumption, v
MAP
becomes
i
j
i
j
V
v
NB
v
a
P
v
P
v
j
)

(
)
(
max
arg
Learning a Naïve Bayes classifier
To
learn
such a classifier: just estimate P(v
j
),
P(a
i
v
j
) from data
How to estimate?
–
simplest: standard estimate from statistics
•
estimate probability from sample proportion
•
e.g., estimate P(AB) as count(A and B) / count(B)
–
in practice, something more complicated
needed…
i
j
i
j
V
v
NB
v
a
P
v
P
v
j
)

(
ˆ
)
(
ˆ
max
arg
Estimating probabilities
Problem:
–
What if attribute value
a
i
never observed for class
v
j
?
–
Estimate P(
a
i
v
j
)=0 because count(
a
i
and
v
j
) = 0 ?
•
Effect is too strong: this 0 makes the whole product 0!
Solution: use m

estimate
–
interpolates between observed value
n
c
/n and a priori
estimate p

> estimate may get close to 0 but never 0
•
m is weight given to a priori estimate
m
n
mp
n
v
a
P
c
j
i
)

(
ˆ
Learning to classify text
Example application:
–
given text of newsgroup article, guess which
newsgroup it is taken from
–
Naïve bayes turns out to work well on this
application
–
How to apply NB?
–
Key issue : how do we represent examples? what
are the attributes?
Representation
Binary classification (+/

) or multiple classes
possible
Attributes = word frequencies
–
Vocabulary = all words that occur in learning task
–
# attributes = size of vocabulary
–
Attribute value = word count or frequency in the
text (using m

estimate)
= “Bag of Words” representation
Algorithm
procedure
learn_naïve_bayes_text(
E
: set of articles, V: set of classes)
Voc = all words and tokens occurring in E
estimate P(v
j
) and P(w
k
v
j
) for all w
k
in E and v
j
in V:
N
j
= number of articles of class j
N = number of articles
P(v
j
) = N
j
/N
n
kj
= number of times word w
k
occurs in text of class j
n
j
= number of words in class j (counting doubles)
P(w
k
v
j
) = (n
kj
+1)/(n
j
+Voc)
procedure
classify_naïve_bayes_text(A: article)
remove from A all words/tokens that are not in Voc
return argmax
vj
V
P(v
j
)
i
P(a
i
v
j
)
Some (old) experimental results:
–
1000 articles taken from 20 newsgroups
–
guess correct newsgroup for unseen documents
–
89% classification accuracy with previous
approach
•
Note: more recent approaches based on SVMs,
… have been reported to work better
–
But Naïve Bayes still used in practice, e.g., for
spam detection
Bayesian Belief Networks
Consider two extremes of spectrum:
–
guessing joint probability distribution
•
would yield optimal classifier
•
but infeasible in practice (too much data needed)
–
Naïve Bayes
•
much more feasible
•
but strong assumptions of conditional independence
•
Is there something in between?
–
make some independence assumptions, but only
where reasonable
Bayesian belief networks
Bayesian belief network consists of
1:
graph
•
intuitively: indicates which variables “directly influence”
which other variables
–
arrow from A to B: A has direct effect on B
–
parents(X) = set of all nodes directly influencing X
•
formally: each node is
conditionally independent of
each of its non

descendants, given its parents
–
conditional independence: cf. Naïve Bayes
–
X conditionally independent of Y given Z iff P(XY,Z) = P(XZ)
2:
conditional probability tables
•
for each node X : P(Xparents(X)) is given
Example
•
Burglary or earthquake may cause alarm to go off
•
Alarm going off may cause one of
neighbours
to
call
Burglary
Earthquake
Alarm
John calls
Mary calls
B,E B,

E

B,E

B,

E
A
0.9 0.8 0.4 0.01

A
0.1 0.2 0.6 0.99
E
0.01

E
0.99
A

A
M
0.9 0.2

M
0.1 0.8
B
0.05

B
0.95
A

A
J
0.8 0.1

J
0.2 0.9
Network topology usually reflects
direct causal
influences
–
other structure also possible
–
but may render network more complex
Mary calls
Earthquake
Burglary
John calls
Alarm
Burglary
Earthquake
Alarm
John calls
Mary calls
Graph + conditional probability tables allow to
construct joint probability distribution of all
variables
–
P(X
1
,X
2
,…,X
n
) =
i
P(X
i
parents(X
i
))
–
In other words:
bayesian belief network carries full
information on joint probability distribution
Inference
Given values for certain nodes, infer probability
distribution for values of other nodes
•
General algorithm quite complicated
–
See
, e.g.,
Russel
&
Norvig
, 1995:
Artificial
Intelligence, a Modern Approach
General case
In general: inference is NP

complete
–
approximating methods, e.g. Monte

Carlo
to be predicted
evidence (observed)
unobserved
Learning
bayesian
networks
•
Assume structure of network given:
–
only conditional probability tables to be learnt
–
training examples may include values for all
variables, or just for some of them
–
when all variables observable:
•
estimating probabilities as easy as for Naïve Bayes
•
e.g. estimate P(AB,C) as count(A,B,C)/count(B,C)
–
when not all variables observable:
•
methods based on gradient descent or EM
•
When structure of network not given:
–
search for structure + tables
•
e.g. propose structure, learn tables
•
propose change to structure, relearn, see whether
better results
–
active research topic
To remember
•
Importance of Bayes’ theorem
•
MAP, ML, MDL
–
definitions, characterising learners from this
perspective, relationship MDL

MAP
•
Bayes optimal classifier, Gibbs classifier
•
Naïve Bayes: how it works, assumptions made,
application to text classification
•
Bayesian networks: representation, inference,
learning
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