Download - Instance Based Learning
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Instance Based Learning
Ata Kaban
The University of Birmingham
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Today we learn: K-Nearest Neighbours Case-based reasoning Lazy and eager learning
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Instance-based learning
One way of solving tasks of approximating discrete or real valued target functions
Have training examples: (xn, f(xn)), n=1..N. Key idea:
– just store the training examples– when a test example is given then find the
closest matches
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1-Nearest neighbour:Given a query instance xq,
• first locate the nearest training example xn
• then f(xq):= f(xn)
K-Nearest neighbour:Given a query instance xq,
• first locate the k nearest training examples • if discrete values target function then take
vote among its k nearest nbrs else if real valued target fct then take the mean of the f values of the k nearest nbrs
k
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The distance between examples
We need a measure of distance in order to know who are the neighbours
Assume that we have T attributes for the learning problem. Then one example point x has elements xt , t=1,…T.
The distance between two points xi xj is often defined as the Euclidean distance:
T
ttjtiji xxd
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2][),( xx
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Voronoi Diagram
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Characteristics of Inst-b-Learning
An instance-based learner is a lazy-learner and does all the work when the test example is presented. This is opposed to so-called eager-learners, which build a parameterised compact model of the target.
It produces local approximation to the target function (different with each test instance)
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When to consider Nearest Neighbour algorithms?
Instances map to points in Not more then say 20 attributes per
instance Lots of training data Advantages:
– Training is very fast– Can learn complex target functions– Don’t lose information
Disadvantages:– ? (will see them shortly…)
n
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twoone
four
three
five six
seven Eight ?
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Training dataNumber Lines Line types Rectangles Colours Mondrian?
1 6 1 10 4 No
2 4 2 8 5 No
3 5 2 7 4 Yes
4 5 1 8 4 Yes
5 5 1 10 5 No
6 6 1 8 6 Yes
7 7 1 14 5 No
Number Lines Line types Rectangles Colours Mondrian?
8 7 2 9 4
Test instance
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Keep data in normalised form
One way to normalise the data ar(x) to a´r(x) is
t
ttt
xxx
'
attributestofmeanx thr
attributestofdeviationndardsta tht
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Normalised training dataNumber Lines Line
types Rectangles Colours Mondrian?
1 0.632 -0.632 0.327 -1.021 No
2 -1.581 1.581 -0.588 0.408 No
3 -0.474 1.581 -1.046 -1.021 Yes
4 -0.474 -0.632 -0.588 -1.021 Yes
5 -0.474 -0.632 0.327 0.408 No
6 0.632 -0.632 -0.588 1.837 Yes
7 1.739 -0.632 2.157 0.408 No
Number Lines Line types
Rectangles Colours Mondrian?
8 1.739 1.581 -0.131 -1.021
Test instance
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Distances of test instance from training data
Example Distanceof testfromexample
Mondrian?
1 2.517 No
2 3.644 No
3 2.395 Yes
4 3.164 Yes
5 3.472 No
6 3.808 Yes
7 3.490 No
Classification
1-NN Yes
3-NN Yes
5-NN No
7-NN No
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What if the target function is real valued?
The k-nearest neighbour algorithm would just calculate the mean of the k nearest neighbours
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Variant of kNN: Distance-Weighted kNN
We might want to weight nearer neighbors more heavily
Then it makes sense to use all training examples instead of just k (Stepard’s method)
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Difficulties with k-nearest neighbour algorithms
Have to calculate the distance of the test case from all training cases
There may be irrelevant attributes amongst the attributes – curse of dimensionality
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Case-based reasoning (CBR)
CBR is an advanced instance based learning applied to more complex instance objects
Objects may include complex structural descriptions of cases & adaptation rules
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CBR cannot use Euclidean distance measures
Must define distance measures for those complex objects instead (e.g. semantic nets)
CBR tries to model human problem-solving– uses past experience (cases) to solve new
problems– retains solutions to new problems
CBR is an ongoing area of machine learning research with many applications
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Applications of CBR Design
– landscape, building, mechanical, conceptual design of aircraft sub-systems
Planning– repair schedules
Diagnosis– medical
Adversarial reasoning– legal
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CBR processNew Case
matching Matched Cases
Retrieve
Adapt?No
Yes
Closest Case
Suggest solution
Retain
Learn
Revise
Reuse
Case Base
Knowledge and Adaptation rules
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CBR example: Property pricingCase Location
codeBedrooms Recep
roomsType floors Cond-
itionPrice£
1 8 2 1 terraced 1 poor 20,500
2 8 2 2 terraced 1 fair 25,000
3 5 1 2 semi 2 good 48,000
4 5 1 2 terraced 2 good 41,000
Case Locationcode
Bedrooms Receprooms
Type floors Cond-ition
Price£
5 7 2 2 semi 1 poor ???
Test instance
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How rules are generated There is no unique way of doing it. Here
is one possibility: Examine cases and look for ones that
are almost identical– case 1 and case 2
• R1: If recep-rooms changes from 2 to 1 then reduce price by £5,000
– case 3 and case 4• R2: If Type changes from semi to terraced then
reduce price by £7,000
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Matching
Comparing test instance – matches(5,1) = 3– matches(5,2) = 3– matches(5,3) = 2– matches(5,4) = 1
Estimate price of case 5 is £25,000
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Adapting
Reverse rule 2– if type changes from terraced to semi then
increase price by £7,000 Apply reversed rule 2
– new estimate of price of property 5 is £32,000
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Learning
So far we have a new case and an estimated price– nothing is added yet to the case base
If later we find house sold for £35,000 then the case would be added– could add a new rule
• if location changes from 8 to 7 increase price by £3,000
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Problems with CBR
How should cases be represented? How should cases be indexed for fast
retrieval? How can good adaptation heuristics be
developed? When should old cases be removed?
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Advantages
A local approximation is found for each test case
Knowledge is in a form understandable to human beings
Fast to train
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Summary
K-Nearest Neighbours Case-based reasoning Lazy and eager learning
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Lazy and Eager Learning
Lazy: wait for query before generalizing– k-Nearest Neighbour, Case based reasoning
Eager: generalize before seeing query– Radial Basis Function Networks, ID3, …
Does it matter?– Eager learner must create global approximation– Lazy learner can create many local
approximations