Data Mining (and machine learning) ROC curves Rule Induction Basics of Text Mining

Data Mining(and machine learning)

ROC curves

Rule InductionBasics of Text Mining

Two classes is a common and special case


Medical applications: cancer, or not?Computer Vision applications: landmine, or not?Security applications: terrorist, or not?Biotech applications: gene, or not?… …


Medical applications: cancer, or not?Computer Vision applications: landmine, or not?Security applications: terrorist, or not?Biotech applications: gene, or not?… …

Predicted Y Predicted N

Actually Y True Positive False Negative

Actually N False Positive True Negative

Two classes is a common and special caseTrue Positive: these are ideal. E.g. we correctly detect cancer





False Positive: to be minimised – cause false alarm – can be better to be safe than sorry, but can be very costly.






False Negative: also to be minimised – miss a landmine / cancer very bad in many applications






False Negative: also to be minimised – miss a landmine / cancer very bad in many applications

True Negative?:




Sensitivity and Specificity: common measures of accuracy in this kind of 2-class tasks




Sensitivity and Specificity: common measures of accuracy in this kind of 2-class task

Sensitivity = TP/(TP+FN) - how much of the real ‘Yes’ cases are detected? How well can it detect the condition? Specificity = TN/(FP+TN) - how much of the real ‘No’ cases are correctly classified? How well can it rule out the condition?




YES

NO

YES

NO

YES

NO

Sensitivity: 100%Specificity: 25%

YES NO

YES

NO

Sensitivity: 93.8%Specificity: 50%

YES

NO

Sensitivity: 81.3%Specificity: 83.3%

YES NO

YES

NO


YES NO

YES

NO

Sensitivity: 100%Specificity: 25%

YES NO

100% Sensitivity means: detects all cancer cases (or whatever) but possibly with many false positives

YES

NO


YES NO

100% Specificity means: misses some cancer cases (or whatever) but no false positives

Sensitivity and Specificity: common measures of accuracy in this kind of 2-class tasks

Sensitivity = TP/(TP+FN) - how much of the real TRUE cases are detected? How sensitive is the classifier to TRUE cases?A highly sensitive test for cancer: if “NO” then you be sure it’s “NO”

Specificity = TN/(TN+FP) - how sensitive is the classifier to the negative cases? A highly specific test for cancer: if “Y” then you be sure it’s “Y”.

With many trained classifiers, you can ‘move the line’ in this way.E.g. with NB, we could use a threshold indicating how much higherthe log likelihood for Y should be than for N

ROC curves

David Corne, and Nick Taylor, Heriot-Watt University - [email protected] slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Rule Induction• Rules are useful when you want to learn a

clear / interpretable classifier, and are less worried about squeezing out as much accuracy as possible

• There are a number of different ways to ‘learn’ rules or rulesets.

• Before we go there, what is a rule / ruleset?

Rules

IF Condition … Then Class Value is …

YES

NO

5

4

3

2

1

00 1 2 3 4 5 6 7 8 9 10 11 12

Rules are Rectangular

IF (X>0)&(X<5)&(Y>0.5)&(Y<5) THEN YES

YES

NO

5

4

3

2

1

00 1 2 3 4 5 6 7 8 9 10 11 12

Rules are Rectangular

IF (X>5)&(X<11)&(Y>4.5)&(Y<5.1) THEN NO

A Ruleset

IF Condition1 … Then Class = A


IF Condition3 … Then Class = B

IF Condition4 … Then Class = C

…

YES

NO

5

4

3

2

1

00 1 2 3 4 5 6 7 8 9 10 11 12

What’s wrong with this ruleset?(two things)

YES

NO

5

4

3

2

1

00 1 2 3 4 5 6 7 8 9 10 11 12

What about this ruleset?

Two ways to interpret a ruleset:


As a Decision List


ELSE IF Condition2 … Then Class = A

ELSE IF Condition3 … Then Class = B

ELSE IF Condition4 … Then Class = C

…

ELSE … predict Background Majority Class


As an unordered set



IF Condition3 … Then Class = B

IF Condition4 … Then Class = C

Check each rule and gather votes for each class

If no winner, predict background majority class

Three broad ways to learn rulesets


1. Just build a decision tree with ID3 (or something else) and you can translate the tree into rules!


2. Use any good search/optimisation algorithm.

Evolutionary (genetic) algorithms are the most

common. You will do this coursework 3.

This means simply guessing a ruleset at random,

and then trying mutations and variants, gradually

improving them over time.


3. A number of ‘old’ AI algorithms exist that still work well, and/or can be engineered to work with an evolutionary algorithm. The basic idea is: iterated coverage

YES

NO

5

4

3

2

1

00 1 2 3 4 5 6 7 8 9 10 11 12

Take each class in turn ..

YES

NO

5

4

3

2

1

00 1 2 3 4 5 6 7 8 9 10 11 12

Pick a random member of that class in the training set

YES

NO

5

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3

2

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00 1 2 3 4 5 6 7 8 9 10 11 12

Extend it as much as possible without including another class

YES

NO

5

4

3

2

1

00 1 2 3 4 5 6 7 8 9 10 11 12


YES

NO

5

4

3

2

1

00 1 2 3 4 5 6 7 8 9 10 11 12


YES

NO

5

4

3

2

1

00 1 2 3 4 5 6 7 8 9 10 11 12


YES

NO

5

4

3

2

1

00 1 2 3 4 5 6 7 8 9 10 11 12

Next class

YES

NO

5

4

3

2

1

00 1 2 3 4 5 6 7 8 9 10 11 12

Next class

YES

NO

5

4

3

2

1

00 1 2 3 4 5 6 7 8 9 10 11 12

And so on…

Text as Data: what and why?

2012

2014

Students’ implementation

choices for DMML CW1

“Word Clouds” - word frequency patterns provides useful information

Classify sentiment

http://www.inside-r.org/howto/mining-twitter-airline-consumer-sentiment

Twitter sentiment

AC

S I

ndex

“Word Clouds” - word frequency patterns provides useful information

…which can be used to predict a class value / category / signal… in this case

• the document(s) are “tweets mentioning our airline over past few hours”• class value is a satisfaction score, between 0 and 1

sentiment map of NYC

http://necsi.edu/research/social/newyork/sentimentmap/

more info from tweets,this time, a “happiness”score.

“similar pages”

Based on distancesbetween word frequencypatterns

Predicting relationship between two people based on their text messages

Can you predict class: Desktop, Laptop or LED-TVfrom word frequencies of product description on amazon ?

So, word frequency is important – does this mean that the most frequent words in a text carry the most useful information about its content/category/meaning?

Zipf’s law -- text of Moby Dick -- http://searchengineland.com/the-long-tail-of-search-12198

http://www.wordfrequency.info/files/entriesWithoutCollocates.txt

Rank Word Part of speech Frequency Dispersion

1 the a 22038615 0.982 be v 12545825 0.973 and c 10741073 0.994 of i 10343885 0.975 a a 10144200 0.986 in i 6996437 0.987 to t 6332195 0.988 have v 4303955 0.979 to i 3856916 0.9910 it p 3872477 0.9611 I p 3978265 0.9312 that c 3430996 0.9713 for i 3281454 0.9814 you p 3081151 0.9215 he p 2909254 0.9416 with i 2683014 0.9917 on i 2485306 0.9918 do v 2573587 0.9519 say v 1915138 0.9520 this d 1885366 0.9621 they p 1865580 0.96

Frequencies of wordsfromhttp://corpus.byu.edu/cocaCorpus of ContemporaryAmerican English450 million wordsfrom fiction books, newspapers,magazines, etc… So, 22,038,615/450,000,000= 4.9% are ‘the’/

http://corpus.byu.edu/coca/


1000 detail n 38750 0.97

1001 method n 42667 0.88

1002 sign v 39418 0.95

1003 somebody p 41639 0.90

1004 magazine n 39197 0.96

1005 hotel n 39642 0.95

1006 soldier n 39552 0.95

1007 reflect v 40687 0.92

1008 heavy j 39160 0.96

1009 sexual j 41865 0.89

1010 cause n 38843 0.96

1011 bag n 40007 0.93

1012 heat n 40363 0.92

1013 fall n 38822 0.96

1014 marriage n 38517 0.96

1015 tough j 39600 0.94


4986 kneel v 5467 0.86

4987 vacuum n 5028 0.93

4988 selected j 5315 0.88

4989 dictate v 4935 0.95

4990 stereotype n 5109 0.92

4991 sensor n 5154 0.91

4992 laundry n 5063 0.93

4993 manual n 5040 0.93

4994 pistol n 5261 0.89

4995 naval j 4990 0.94

4996 immigrant j 0.97

4996 plaintiff n 5312 0.88

4997 kid v 5094 0.92

4998 middle-class j 5025 0.93

4999 apology n 4972 0.94

5000 till i 5079 0.92

Zipf’s law -- text of Moby Dick -- http://searchengineland.com/the-long-tail-of-search-12198

the frequency of a specific word in text X is important, but, only if it is not similarly frequent in other texts – in which case it carries little information about X

Which leads us to TFIDF

We can do any kind of DMML with text, as soon as we convert text into numbers

-This is usually done with a “TFIDF” encoding

-and almost always done with either TFIDF or a close relation

-TFIDF is basically word frequency, but takes into account ‘background frequency’ – so words that are very common have their value reduced.

A one-slide text-mining tutorial

an essay about sport

an article about poltics

another article about

politics

(0.1, 0.2, 0, 0.02 ...)

(0.4, 0, 0.1, 0 ...)

(0.11, 0.3, 0, 0.01 ..)

NOW you can do

Clustering,

Retrieving similarDocuments,

Supervised Classification

Etc...Vectors based on word frequencies.

One key issue is to choose the right set of words (or other features)

First, a quick illustration to show why word-frequency vectors are useful

How did I get these vectors from these two `documents’?

<h1> Compilers: lecture 1 </h1><p> This lecture will introduce theconcept of lexical analysis, in whichthe source code is scanned to revealthe basic tokens it contains. For this,we will need the concept of regular expressions (r.e.s).</p>

<h1> Compilers</h1><p> The Guardian uses severalcompilers for its daily crypticcrosswords. One of the most frequently used is Araucaria,and one of the most difficultis Bunthorne.</p>

35, 2, 0 26, 2, 2

What about these two vectors?

<h1> Compilers: lecture 1 </h1><p> This lecture will introduce theconcept of lexical analysis, in whichthe source code is scanned to revealthe basic tokens it contains. For this,we will need the concept of regular expressions (r.e.s).</p>

<h1> Compilers</h1><p> The Guardian uses severalcompilers for its daily crypticcrosswords. One of the most frequently used is Araucaria,and one of the most difficultis Bunthorne.</p>

0, 0, 0, 1, 1, 1 1, 1, 1, 0, 0, 0

From this MASTER WORD LIST (ordered)

(Crossword, Cryptic, Difficult, Expression, Lexical, Token)

If a document contains `crossword’, it gets a 1 in position 1 of the vector, otherwise 0. If it contains `lexical’, it gets a 1 in position 5, otherwise 0, and so on.

How similar would be the vectors for two docs about crossword compilers?

.

Turning a document into a vector

We start with a template for the vector, which needs a master list of terms . A term can be a word, or a number, or anything that appears frequently in documents.

There are almost 200,000 words in English – it would take much toolong to process documents vectors of that length.

Commonly, vectors are made from a small number (50—1000) ofmost frequently-occurring words.

However, the master list usually does not include words from a stoplist,Which contains words such as the, and, there, which, etc … why?

The TFIDF Encoding(Term Frequency x Inverse Document Frequency)

A term is a word, or some other frequently occuring itemGiven some term i, and a document j, the term count is the number of times that term i occurs in document jGiven a collection of k terms and a set D of documents, the term frequency,

is:

… considering only the terms of interest, this is the proportion of document j that is made up from term i.

ijn

ijtf

T

kkj

ijij

n

ntf

1

The TFIDF Encoding(Term Frequency x Inverse Document Frequency)

A term is a word, or some other frequently occuring itemGiven some term i, and a document j, the term count is the number of times that term i occurs in document jGiven a collection of k terms and a set D of documents, the term frequency,

is:

frequency of this word in this doc total number of words in this doc

… considering only the terms of interest, this is the proportion of document j that is made up from term i.

ijn

ijtf

T

kkj

ijij

n

ntf

1

Some made-up data for illustration: TF vectors

money interest EU designer wear CATEGORY

0.03 0.04 0.08 0 0.01 Economics

0.02 0.06 0.09 0 0 Economics

0.04 0.04 0.02 0.01 0.02 Fashion

0 0.03 0 0.01 0.02 Fashion

Term frequency is a measure of the importance of this term in this document

Inverse document frequency (which we see next) is a measure of the discriminatory value of the term in the collection of documents we are looking at..

It is a measure of the rarity of this word in this document collection

E.g. high term frequency for “money” means that money is an important word in a specific document.

But high document frequency (low inverse document frequency) for “money”, given a particular set of documents, means that money does not carry much useful information, since it is in many of the documents.

ijtf

Inverse document frequency of term i is:

where D is a master collection of documents – and C are thesubset of D that contain term i at least once.

E.g. if we are trying to learn a classifier of news articles into ‘sport’, ‘economics’, etc… D might be a set of 100,000 news articles

Often, we simply replace idf with ‘background frequences’ obtainedFrom a corpus such as the CCA corpus

||

||log

C

Didf

TFIDF encoding of a documentSo, given: - a background collection of documents (e.g. 100,000 random web pages, all the articles we can find about cancer 100 student essays submitted as coursework …) - a specific ordered list (possibly large) of terms We can encode any document as a vector of TFIDF numbers, where the ith entry in the vector for document j is:

iij idftf

Some made-up data for illustration: now they are TFIDF vectors,and some have reduced more than others

Money interest EU designer wear CATEGORY

0.0075 0.04 0.07 0 0.01 Economics

0.005 0.06 0.08 0 0 Economics

0.01 0.04 0.01 0.01 0.02 Fashion

0 0.03 0 0.01 0.02 Fashion

Vector representation of documents underpins:

Many areas of automated document analysis

Such as: automated classification of documents

Clustering and organising document collections

Building maps of the web, and of different web communities

Understanding the interactions between different scientific communities, which in turn will lead to helping with automated WWW-based scientific discovery.

Example / recent work of my PhD student Hamouda Chantar

Three datasets / classification / main issue: Feature Selection

Dataset Articles in

Train / Test

categories

Distinct words in training set

Al-Jazeera News

1200 / 300 5 5,329

Alwatan 821 / 352 4 12,282Akhbar -Alkhaleej

1365 / 343 4 8,913

Hamouda’s work

Focus on automated classification of an article (e.g. Finance, Economics, Sport, Culture, ...)

Emphasis on Feature Selection – which words or other features should constitute the vectors, to enable accurate classification?

Example categories:this is the Akhbar-Alkhaleej dataset

Category Train Test Total

International News

228 58 286

Local news 576 144 720

Sport 343 86 429Economy 218 55 273Total 1365 343 1708

We look at 3 pre-classified datasets

Akhbar-Alkhaleej: 5690 Arabic news documents gathered evenly from the online newspaper "Akhbar-Alkhaleej"

Alwatan: 20,291 Arabic news documents gathered from online newspaper "Alwatan”

Al-jazeera-News:1500 documents from the Al-Jazeera news site.

is.gd/arabdata

We look at 3 classification methods(when evaluating feature subsets on the

test set)

C4.5: well-known decision tree classifier, we use weka’s implementation, “J48”

Naive Bayes: It’s Naive, and it’s Bayes

SVM: with a linear kernel

Results: Alwatan dataset

Results on Al Jazeera dataset

Results: Akhbar-Alkhaleej dataset

tara