ApMl (All Purpose Machine
Learning) Toolkit
David W. Miller and Helen Howell
Semantic Web Final Project
Spring 2002
Department of Computer Science
University of Georgia
www.cs.uga.edu/~miller/SemWeb
www.cs.uga.edu/~helen/SemWeb/SemWeb.html
2
What Has Been Done
•
Extensive Research into the
effectiveness of machine learning
algorithms has been performed
–
Train System on expert created taxonomy
with expert specified documents
3
What We Did
•
Train system on a domain specific
taxonomy
–
Eg. CNN’s Sports Pages
•
Test system’s ability to correctly classify
documents from a second, yet similar
taxonomy
–
Eg. Yahoo! Sports Pages
4
Automatic Text Classification
via Statistical Methods
Text Categorization is the problem of assigning
predefined
categories to free text documents.
Statistical Learning Methods used in ApMl
•
Bayes Method
•
Rocchio Method (most popular)
•
K

Nearest Neighbor Classification
•
Probabilistic Indexing
5
A Probabilistic Generative Model
•
Define a probabilistic generative model for
documents with classes.
Bayes:
Reinforcement
Learning:
a Survey
This paper surveys
the field of rein

forcement learning
from a computer
science perspective.
35 a
1 block
12 computer
4 field
1 leg
7 machine
44 of
3 paper
2 perspective
1 rate
5 reinforcement
9 science
2 survey
56 the
11 this
1 underrated
… …
“Bag

of

words”
Automatic Text Classification through Machine Learning, McCallum, et. al.
6
Bayes Method
Pick the most probable class, given the evidence:

a class (like “
Planning
”)

a document (like “
language intelligence proof
...
”)
Bayes Rule:
Probability Category c
j
should be assigned to
document d
Automatic Text Classification through Machine Learning, McCallum, et. al.
7
Bayes Rule

Probability that document
d
belongs to category
c
j

Probability that a randomly picked document has the same attributes

Probability that a randomly picked document belongs to this category

Probability that category
c
contains document
d
8
Bayes Method
•
Generates conditional probabilities of
particular words occurring in a document
given it belongs to a particular category.
•
Larger vocabulary generate better
probabilities
•
Each category is given a threshold
p
for
which it judges the worthiness of a document
to fall in that classification.
•
Documents may fall into one, more than one,
or not even one category.
9
Rocchio Method
•
Each document is
D
is represented as a
vector within a given vector space
V
:
•
Documents with similar content have similar
vectors
•
Each dimension of the vector space represents a
word selected via a feature selection process
10
Rocchio Method
•
Values of
d
(i)
for a document
d
are
calculated as a combination of the
statistics
TF(w,d) and DF(w)
•
TF(w,d)
(Term Frequency) is the
number of times word
w
occurs in a
document
d.
•
DF(w)
(Document Frequency) is the
number of documents in which the word
w
occurs at least once.
11
Rocchio Method
•
The
inverse document frequency
is
calculated as
•
Value of
d
(i)
of feature
w
i
for a document
d
is
calculated as the product
•
d
(i)
is called the weight of the word
w
i
in the
document
d.
12
Rocchio Method
•
Based on word weight heuristics, the
word w
i
is an important indexing term
for a document
d
if it occurs frequently
in that document
•
However, words that occurs frequently
in many document spanning many
categories are rated less importantly
13
K

Nearest Neighbor
•
Features
–
All instances correspond to points in an n

dimensional Euclidean space
–
Classification is delayed till a new instance
arrives
–
Classification done by comparing feature
vectors of the different points
–
Target function may be discrete or real

valued
K

Nearest Neighbor Learning, Dipanjan Chakraborty
14
1

Nearest Neighbor
K

Nearest Neighbor Learning, Dipanjan Chakraborty
15
K

Nearest Neighbor
•
An arbitrary instance is represented by
(a
1
(x), a
2
(x), a
3
(x),.., a
n
(x))
–
a
i
(x) denotes features
•
Euclidean distance between two instances
d(x
i
, x
j
)=sqrt (sum for r=1 to n (a
r
(x
i
)

a
r
(x
j
))
2
)
•
Find the k

nearest neighbors whose distance
from your test cases falls within a threshold
p.
•
If
x
of those k

nearest neighbors are in
category
c
i
, then assign the test case to
c
i
,
else it is unmatched.
K

Nearest Neighbor Learning, Dipanjan Chakraborty
16
Probabilistic Indexing
•
Goal is to estimate P(Cs
i
, d
m
)
–
Probability that assignment of term s
i
to the
document d
m
is correct
•
Once terms have been identified, assign
Form Of Occurrence (FOC)
–
Certainty that term is correctly indentified
–
Significance of Term
17
Probabilistic Indexing Cont.
•
If term
t
appears in document
d
and a
term descriptor from
t
to
s
exists,
s
an
indexing term, then generate a
descriptor indictor
•
Set of generated term descriptors can
be evaluated and a probability
calculated that document
d
lies in class
c
18
ApMl Toolkit
•
Built on top of and extends existing
toolkits
–
rainbow (CMU)
–
Machine Learning
–
wget (GNU)
–
Web Crawler
•
4 Machine Learning Algorithms and 2
Classification Committees
•
Web Crawler and Document Retrieval
•
Automated Testing
19
Machine Learning Components
•
4 Machine Learning Algorithms
(rainbow)
–
Naïve Bayes, Rocchio, KNN, Probabilistic
Indexing
•
2 Classification Committees (ApMl)
–
Weight Assigned For Overall Accuracy
–
Weights Assigned For Accuracy within
each Class of Taxonomy
20
21
22
Document Retrieval
•
Web Crawler and Document Retrieval
–
Specify Starting URL
–
Specify Recursion Depth
–
Allow Multiple Domain Spanning
–
Specify Excluded Domains
–
Store all retrieved pages into a single
directory (ApMl)
23
24
Automated Testing
•
Choose Algorithms to Test
•
Choose Test Directory
•
Specify Number of Tests
•
All results are placed into persistent
window for evaluation
25
26
Effectiveness: Contingency
Table
Machine Learning for Text Classification, David D. Lewis, AT&T Labs
27
•
precision =
a/(a+b)
–
Documents classified correctly vs. All classified as a
particular category
•
recall =
a/(a+c)
–
Documents classified correctly vs. All that should have been
classified in a category
•
accuracy = (
a+d)/(a+b+c+d)
–
All documents classified as positive or negative in a category
correctly vs All classified
Effectiveness Measures
Machine Learning for Text Classification, David D. Lewis, AT&T Labs
28
Test Plan
•
Choose two areas and selected
subcategories
–
Sports
•
Football
•
Tennis
•
Golf
•
NBA
–
Health
•
Children
•
Men
•
Women
29
Test Plan Continued
•
Sport Web Sites
–
www.sportsillustrated.com
–
sports.yahoo.com
–
www.usatoday.com/sports/sfront.htm
•
Health Web Sites
–
www.patient.co.uk
–
www.cdc.gov/health
–
www.bbc.co.uk/health
30
Test Plan Continued
•
Train the system on pages from one
taxonomy from one domain and test on
another taxonomy for the same area
•
Determine contingency tables for each
category
•
Compute effectiveness using precision,
recall, and accuracy
31
Sports Test Results
ApMl Test Results
32
Health Test Results
ApMl Test Results
33
Comparison of Precision
ApMl Test Results
34
Comparison of Recall
ApMl Test Results
35
Comparison of Sports
Additional Levels
ApMl Test Results
36
Comparison of Health
Additional Levels
ApMl Tests Results
37
Comparison of Accuracy
ApMl Test Results
38
Trends of Results
•
K Nearest Neighbor effectiveness was
significantly lower than other algorithms
–
continuously categorize the same
•
The class of Health was much more
difficult for the algorithms to correctly
categorize
–
children’s health a non

gender class
•
No improvement in our results with
additional training
39
Conclusions
•
Results of automatic text categorization
are subjective
•
Trends can occur because of various
factors
•
Heterogeneous taxonomies can be
used for automatic classification with
acceptable efficiencies
•
More research needed
40
Resources
1.
Dipanjan Chakraborty. “K

Nearest Neighbor Learning.” A
PowerPoint Presentation.
2.
Norbert Fuhr and Ulrich Pfeifer. “Combining Model

Oriented
and Description

Oriented Approached for Probabilistic
Indexing.”
Proceedings of the Fourteenth Annual
International ACM SIGIR Conference on Research and
Development in Information Retrieval
, pages 46

56. ACM,
New York. 1991.
3.
Thorsten Joachims. “A Probabilistic Analysis of the Rocchio
Algorithm with TFIDF for Text Categorization.” Technical
Report, CMU, March 1996.
4.
Fabrizio Sebastiani. “Machine Learning in Automated Text
Categorization.”
ACM Computing Surveys
, 34(1):1

47, 2002.
5.
Amit Sheth, et. al. “Semantic Web Content Management for
Enterprises and the Web.”
In submission to IEEE Internet
Computing.
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