instance based and bayesian learning

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 <xi,yi>

2. Classify a new example x by finding the stored example xk that most resembles it and predicts that example’s class yk

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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

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Voronoi diagram

Boundaries are not

computed!

Keeping All Information

Advantage: no details lost Disadvantage: "details" may be noise

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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

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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 dataDecision tree algorithms, neural networks, …– Learning can be slow– Predictions are usually fast!

Similarity measures

Distance metrics: measure of dis-similarityE.g. Manhattan, Euclidean or Ln-norm for numerical

attributes

Hamming distance for nominal attributes

Distance definition = critical!

E.g. comparing humans1. 1.85m, 37yrs2. 1.83m, 35yrs3. 1.65m, 37yrs

d(1,2) = 2.00…0999975…

d(1,3) = 0.2d(2,3) = 2.00808…

1. 185cm, 37yrs2. 183cm, 35yrs3. 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 mi the minimum and Mi the maximum value for attribute 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

Curse of Dimensionality (2)

With 5000 points in 10 dimensions, each attribute range must be covered approx. 50% to find 5 neighbors …

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Curse of Noisy Features

Irrelevant features destroy the metric’s meaningfulnessConsider a 1dim problem where the query x is at the origin,

the nearest neighbor x1 is at 0.1 and the second neighbor x2 at 0.5 (after normalization)

Now add a uniformly random feature. What is the probability that x2 becomes the closest neighbor?

approx. 15% !!

Curse of Noisy Features (2)

Location of x1 vs x2 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 otherAllowed edits/changes:1.delete character2.insert character3.change character

(not used by some other edit-distances)

Even more distances

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iii cqCQD

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2,

Q

C

D(Q,C)

Given two time series: Q = q1…qn

C = c1…cn

Euclidean

Start and end times are critical!

D(Q,R)

R

Sequence distances (2)

Fixed Time AxisSequences are aligned “one to one”.

“Warped” Time AxisNonlinear alignments are possible.

edit

distance!

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”)

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! 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 dataIncremental deletion

of examples

Incremental

addition of

examples

✔ 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)

Moving away from

lazy learning

Moving away from

lazy learning

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

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Prototypes (cont.)

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

-> in this case: need to define distance

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Moving further and

further away from

lazy learning

Moving further and

further away from

lazy learning

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:

Avg. rating

of user i can be allentries or kNN

rating by user jof entry k

Pearson coefficient

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(A|B): 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(B|A)

= P(A) P(B) if A and B independent– total probability:P(A) = i P(A|Bi) P(Bi)

With each Bi mutually exclusive

Bayes’ Theorem

P(A|B) = P(B|A) P(A) / P(B)

Mainly 2 ways of using Bayes’ theorem:– Applied to learning a hypothesis h from data D:

P(h|D) = P(D|h) P(h) / P(D) ~ P(D|h)P(h)– P(h): a priori probability that h is correct– P(h|D): a posteriori probability that h is correct– P(D): probability of obtaining data D– P(D|h): probability of obtaining data D if h is correct

– Applied to classification of a single example e:P(class|e) = P(e|class)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(Dis|Pos) = P(Pos|Dis)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 hH that has the highest probability of being correct; i.e., P(h|D) is maximal

This hypothesis is called the maximal a posteriori hypothesis hMAP : hMAP = argmaxhH P(h|D)

= argmaxhH P(D|h)P(h)/P(D) = argmaxhH P(D|h)P(h)

•last equality holds because P(D) is constant

So : we need P(D|h) and P(h) for all hH to compute hMAP

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 hMAP is called the maximum likelihood hypothesis hML

hML = argmaxhH P(D|h) (because P(h) constant)•How to find hMAP or hML ?– brute force method: compute P(D|h), P(h) for all hH– 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(a1…an|vj)

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MAP

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The Naïve Bayes classifier

Naïve Bayes assumption: attributes are independent, given the class

P(a1,…,an|vj) = P(a1|vj)P(a2|vj)…P(an|vj)–also called conditional independence (given the class)

•Under that assumption, vMAP becomes

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VvNB vaPvPv

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Learning a Naïve Bayes classifier

To learn such a classifier: just estimate P(vj), P(ai|vj) from data

How to estimate?– simplest: standard estimate from statistics• estimate probability from sample proportion• e.g., estimate P(A|B) as count(A and B) / count(B)

– in practice, something more complicated needed…

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NB vaPvPvj

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Estimating probabilities

Problem:– What if attribute value ai never observed for class vj?

– Estimate P(ai|vj)=0 because count(ai and vj) = 0 ?• Effect is too strong: this 0 makes the whole product 0!

Solution: use m-estimate– interpolates between observed value nc/n and a priori

estimate p -> estimate may get close to 0 but never 0• m is weight given to a priori estimate

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mpnvaP cji

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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 Eestimate P(vj) and P(wk|vj) for all wk in E and vj in V:

Nj = number of articles of class jN = number of articlesP(vj) = Nj/Nnkj = number of times word wk occurs in text of class jnj = number of words in class j (counting doubles)P(wk|vj) = (nkj+1)/(nj+|Voc|)

procedure classify_naïve_bayes_text(A: article)remove from A all words/tokens that are not in Vocreturn argmaxvjV P(vj) i P(ai|vj)

procedure learn_naïve_bayes_text(E: set of articles, V: set of classes)Voc = all words and tokens occurring in Eestimate P(vj) and P(wk|vj) for all wk in E and vj in V:

Nj = number of articles of class jN = number of articlesP(vj) = Nj/Nnkj = number of times word wk occurs in text of class jnj = number of words in class j (counting doubles)P(wk|vj) = (nkj+1)/(nj+|Voc|)

procedure classify_naïve_bayes_text(A: article)remove from A all words/tokens that are not in Vocreturn argmaxvjV P(vj) i P(ai|vj)

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 of1: 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(X|Y,Z) = P(X|Z)

2: conditional probability tables• for each node X : P(X|parents(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(X1,X2,…,Xn) = i P(Xi|parents(Xi))

– 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(A|B,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