2. CONTENTS
Introduction to SVM
What is SVM?
Works on SVM
Linear classifiers
Non linear classifiers
Introduction to associative classification
Association based classification
Associative classification
Why is effective?
Types of associative classification method
Conclusion
3. INTRODUCTION TO SVM
SVM is a classifier derived from statistical learning
theory by Vapnik and Chervonenkis..
SVMs introduced by Boser,Guyon,Vapnik in COLT-
92.
Initially popularized in the NIPS community ,now an
important and active field of all machine learning
research.
Special issues of Machine Learning Journal, and
Journal of Machine Learning Research.
4. WHAT IS SVM?
SVMs are learning system that
Use hypothesis space of linear functions
In a high dimensional feature space - kernel function.
Trained with a learning algorithm from optimization
theory –Lagrange
Implements a learning bias derived from statistical
learning theory –Generalisation SVM is a classifier
derived from statistical learning theory by Vapnik and
Chervonenkis.
7. NON –LINEAR CLASSIFICATION
The problem
The maximal margin classifier is an
important concept, but it cannot be used in
many real-word problems.
There will in general be no linear
separation in the feature space.
The solution
Maps the data into another space that can be
separated linearly.
8. A LEARNING MACHINE
A learning machine F takes an input X and
transform it, somehow using weights ,into a
predicted output
9. THE CONCEPT OF HYPERPLANE
For a binary linear
separable training set,
we can find at least a
hyperplane(w , b) which
divides the space into
two half space.
The definition of
hyperplane f(x)=0.
10. THE MAXIMAL MARGIN
CLASSIFIER
The simple model of SVM
Finds the maximal margin hyperplane
in an chosen Kernel-induced feature
space.
A convex optimization problem
Minimizing a quadratic function under
linear inequality constrains.
11.
12. WORKS AN SVM
SVM or Support Vector Machine is a linear
model for classification and regression
problems. It can solve linear and non-linear
problems and work well for many practical
problems. The idea of SVM is simple: The
algorithm creates a line or a hyperplane
which separates the data into classes.
13. INTRODUCTION TO
ASSOCIATIVE CLASSIFICATION
Associative classification(AC) is a branch of a
wide area of scientific study known as data mining.
Associative classification makes use of associative
rule mining for extracting efficient rules, which
can precisely generalize the training data set, in the
rule discovery process .
14.
15. ASSOCIATION BASED
CLASSIFICATION
Classification using association rules combines
association rule mining and classification ,and is
therefore concerned with finding rules that
accurately predict a single target variable .the key
strength of association rule that all interesting rules
are found.
16. ASSOCIATIVE CLASSIFICATION
Association rules are generated and analyzed for
use in classification.
Search for strong associations between frequent
patterns (conjunctions of attributed –value pairs)
and class labels.
Classification: Based on evaluating a set of rules in
the form of
P1^P2…….^ Pi->”A class = c”(confi,sup)
17. WHY EFFECTIVE ?
It explores highly confident associations among
multiple attributes and may overcome some
constraints introduced by decision-tree induction,
which consider only one attributes at a time.
In many studies ,associative classification has been
found to be more accurate than some traditional
classification methods.
18. TYPES ASSOCIATIVE
CLASSIFICATION METHOD
CBA(classification by association:Liu,Hsu&Ma,
KDD’98).
Mine association possible rules in the form of,
cond_set-> class label
Build classifier: organize rules according to
decreasing precedence based on confidence and
then support .
19. CONT……
CMR( classification based on multiple association
rules:Li,Han,Pei).
Generation of predictive rules (Foil-like analysis).
High efficiency, accuracy similar to CMR.
20. CONCULSIONS
Associative classification is a promising approach
in data mining.
Since more than LLHs could improve the
objective function in the hyperheuristic, we need
a multi-label rules in the classifier.
Associative classifiers produce more accurate
classification models than traditional classification
algorithm such as decision trees and trees and
rule induction approaches.
One challenge in associative classification is the
exponential growth of rules of rules, therefore
pruning becomes essential.
