Learning Agents Laboratory

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CS 782 Machine Learning
9 Instance-Based Learning
Prof. Gheorghe Tecuci
Learning Agents Laboratory Computer Science
Department George Mason University
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Overview
Exemplar-based representation of concepts
The k-nearest neighbor algorithm
Discussion
Lazy Learning versus Eager Learning
Recommended reading
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Concepts representation
Let us consider a set of concepts C c1, c2,
... , cn, covering a universe of instances I.
Each concept ci represents a subset of I.
How is a concept usually represented?
How does one test whether an object a is an
instance of a concept c1?
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Intentional representation of concepts
How is a concept usually represented?
Usually, a concept is represented intentionally
by a description covering the positive examples
of the concept and not covering the negative
examples.
How does one test whether an object a is an
instance of a concept ci?
The set of instances represented by a concept ci
is the set of instances of the description of ci.
Therefore, testing if an object a is an instance
of a concept ci reduces to testing if the
description of ci is more general than the
description of a.
How could we represent a concept extensionally?
How could we represent a concept extensionally,
without specifying all its instances?
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Exemplar based representation of concepts

• A concept ci may be represented extensionally by
• a collection of examples ci ei1, ei2, ...,
• a similarity estimation function f, and
• a threshold value q.

An instance a belongs to the concept ci if a
is similar to an element eij of ci, and this
similarity is greater than q, that is, f(eij,
ci) gt q.
How could a concept ci be generalized in this
representation?
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Generalization in exemplar based representations
How could a concept ci be generalized in this
representation?

• Generalizing the concept ci may be achieved by
• adding a new exemplar
• decreasing q.

Why are these generalization operations?
Is there an alternative to considering the
threshold value q for classification of an
instance?
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Prediction with exemplar based representations
Let us consider a set of concepts C c1, c2,
... , cn, covering a universe of instances I.
Each concept ci is represented extensionally as
a collection of examples ci ei1, ei2, ....
Let a be an instance to classify. How to
decide to which concept does a
belong? Different answers to this question lead
to different learning methods.
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Prediction (cont)
Let a be an instance to classify in one of the
classes c1, c2, ... , cn. How to decide to
which concept does it belong?
Method 1 a belongs to the concept ci if a is
similar to an element eij of ci, and this
similarity is greater than the similarity between
a and any other concept exemplar (1-nearest
neighbor).
What is a potential problem with 1-nearest
neighbor?
Hint Think of an exemplar which is not typical.
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Prediction (cont)
How could the problem with method 1 be alleviated?
Use more than one example.
Method 2 Consider the k most similar exemplars.
a belongs to the concept ci that contains most
of the k exemplars (k-nearest neighbor).
What is a potential problem with k-nearest
neighbor?
Hint Think of the intuition behind
instance-based learning.
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Prediction (cont)
How could the problem with method 2 be alleviated?
Weight the exemplars.
Answer 3 Consider the k most similar exemplars,
but weight their contribution to the class of a
by their distance to a, giving greater weight
to the closest neighbors (distance-weighted
nearest neighbor).
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Overview
Exemplar-based representation of concepts
The k-nearest neighbor algorithms
Discussion
Lazy Learning versus Eager Learning
Recommended reading
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The k-nearest neighbor algorithm
Each example is represented using the
feature-vector representation ei (a1vi1,
a2vi2, , anvin) The distance between two
examples ei and ej is the Euclidean distance
d(ei, ej) vS (vik - vjk)2

Training algorithm Each example is represented as
a feature-value vector. For each training example
(eik Ci) add eik to the exemplars of
Ci. Classification algorithm Let a be an
instance to classify. Find the k most similar
exemplars. Assign a to the concept that
contains the most of the k exemplars.
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Nearest neighbors algorithms illustration
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q1
e1

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1-nearest neighbor the concept represented by e1
5-nearest neighbors q1 is classified as negative
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Overview
Exemplar based representation of concepts
The k-nearest neighbor algorithms
Discussion
Lazy Learning versus Eager Learning
Recommended reading
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Nearest neighbors algorithms inductive bias
What is the inductive bias of the k-nearest
neighbor algorithm?
The assumption that the classification of an
instance a will be most similar to the
classification of other instances that are nearby
in the Euclidian space.
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Application issues
Which are some practical issues in applying the
k-nearest neighbor algorithms?
Because the distance between instances is based
on all the attributes, less relevant attributes
and even the irrelevant ones are used in the
classification of a new instance.
Because the algorithm delays all processing until
a new classification/prediction is required,
significant processing is needed to make the
prediction.
Because the algorithm is based on a distance
function, the attribute values should be such
that a distance could be computed.
How to alleviate these problems?
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Application issue the use of the attributes
The classification of an example is based on all
the attributes, independent of their relevance.
Even the irrelevant attributes are used.
How to alleviate this problem?
Weight the contribution of each attribute, based
on its relevance.
How to determine the relevance of an attribute?
Use an approach similar to cross-validation.
How?
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Application issue processing for classification
Because the algorithm delays all processing until
a new classification/prediction is required,
significant processing is needed to make the
prediction.
How to alleviate this problem?
Use complex indexing techniques to facilitate the
identification of the nearest neighbors at some
additional cost in memory.
How?
Tress where the leaves are exemplars, nearby
exemplars are stored at nearby nodes, and
internal nodes sort the query to the relevant
leaf by testing selected attributes.
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Instance-based learning discussion
Which are the advantages of the instance-based
learning algorithms?
Which are the disadvantages of the instance-based
learning algorithms?
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Instance-based learning advantages
Model complex concept descriptions using simpler
example descriptions.
Information present in the training examples is
never lost, because the examples themselves are
stored explicitly.
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Instance-based learning disadvantages
Efficiency of labeling new instances is low,
because all processing is done at prediction time.
It is difficult to determine an appropriate
distance function, especially when examples are
represented as complex symbolic expressions.
Irrelevant features have a negative impact of on
the distance metric.
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Lazy Learning versus Eager Learning

Lazy learning Defer the decision of how to
generalize beyond the training data until each
new query instance is encountered.
Eager learning Generalizes beyond the training
data before observing the new query, committing
at the training time to the learned concept.

How do the two types of learning compare in terms
of computation time?
Lazy learners require less computation time for
training and more for prediction.
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Exercise
Suggest a lazy version of the eager decision tree
learning algorithm ID3. What are the advantages
and disadvantages of your lazy algorithm compared
to the original eager algorithm?
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Recommended reading
Mitchell T.M., Machine Learning, Chapter 8
Instance-based learning, pp. 230 - 248, McGraw
Hill, 1997. Kibler D, Aha D., Learning
Representative Exemplars of Concepts An Initial
Case Study, in J.W.Shavlik, T.G.Dietterich (eds),
Readings in Machine Learning, Morgan Kaufmann,
1990.