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Presentation
on
Boosting Approach For Classification Problems
Presenter:
Prithvi Raj Paneru
M.Sc. CSIT(2013-15)
Roll no: 1
1. Introduction
2. Combining Classifiers
3. Bagging
4. Boosting
5. AdaBoost Algorithm
6. Conclusion
10. References
Overview
Supervised learning is the machine learning task .
 infer a function from labeled training data.
The training data consist of a set of training examples.
In supervised learning, each example is a pair
consisting of a input object and a desired output
value called a supervisory signal.
Optimal scenario ?
Target: generalize the learning algorithm from the
training data to unseen situation in reasonable way.
Introduction
 Classification is a type of supervised learning.
 Classification relies on a priori reference structures that
divide the space of all possible data points into a set of
classes that are usually, but not necessarily, non-
overlapping.
 A very familiar example is the email spam-catching
system.
Classification
 The main issue in the classification is miss
classification.
 which leads to the under-fitting and over-fitting
problems.
 Like in the case of spam filtering due to miss
classification the spam may be classified as not spam
which is not considerable sometime.
 So the major issue here to improve the accuracy of
the classification.
Contd……
Combining classifiers makes the use of some weak
classifiers and combining such classifier gives a strong
classifier.
Combining Classifiers
Contd…….
Bagging (Bootstrap aggregating) operates using
bootstrap sampling.
Given a training data set D containing m examples,
bootstrap sampling draws a sample of training
examples, Di, by selecting m examples uniformly at
random with replacement from D. The replacement
means that examples may be repeated in Di.
Bagging
Contd…..
Training Phase
Initialize the parameters
D={Ф}
h=the number of classification
For k=1 to h
Take a bootstrap sample Sk from training set S
Build the classifier Dk using Sk as training set
D=DUDi
Return D
Classification Phase
Run D1,D2,………..Dk on the input k
The class with maximum number of vote is choosen as the label
for X.
Bagging Algorithm
Boosting has been a very successful technique for solving the
two-class classification problem.
It was first introduced by Freund & Schapire (1997), with their
AdaBoost algorithm .
Rather than just combining the isolated classifiers boosting use
the mechanism of increasing the weights of misclassified data in
preceding classifiers.
A weak learner is defined to be a classifier which is only slightly
correlated with the true classification.
In contrast, a strong learner is a classifier that is arbitrarily well-
correlated with the true classification.
Boosting
Contd……
1. Initialize the data weighting coefficients {Wn } by setting Wi =
1/n, for n=1,2……..,N
2. For m=1 to m
a. Fit a classifier y 𝑚(x) to the training data by minimizing the
weighted error function.
b. Evaluate the quantities
The term I(ym(xn)≠tn) is indication function has values 0/1, 0 if xn
is properly classified 1 if not so.
AdaBoost Algotithm
And use these to evaluate
c. Update the data weighting coefficients
3. Make predictions using the final model, which is given by
Contd….
 Let us take following points training set having 10 points represented
by plus or minus.
 Assumption is the original status is assign equal weight to all points.
 Let us take following points training set having 10 points represented
by plus or minus.
 Assumption is the original status is assign equal weight to all points.
 i.e. W1
(1) =W1
(2 ) =…………….=W1
(10)=1/10.
 Figure1. Training set consisting 10 samples
Example AdaBoost
Round 1: Three “plus” points are not correctly classified. They
are given higher weights.
Figure 2. First hypothesis h1 misclassified 3 plus.
Contd…..
And error term and learning rate for first hypothesis as:
𝜖1 =
0.1+0.1+0.1
1
= 0.30
𝛼1 =
1
2
ln 1 − 0.30
0.30
= 0.42
Now we calculate the weights of each data points for second hypothesis as:
Wn
(m+1)=?
1st, 2nd, 6th, 7th, 8th, 9th and 10th data points are classified properly so their
weight remains same.
i.e. W1
(2)=W2
(2)=W6
(2)=W7
(2)=W8==W9
(2)=W10
(2)= 0.1
but 3rd,4th and 5th data points are misclassified so higher weights are
provided to them as
W3
(2)=W4
(2)=W5
(2)=0.1*e0.42=0.15
Contd..
Round 2: Three “minuse” points are not correctly classified. They
are given higher weights.
Figure5. Second Hypothesis h2 misclassified 3 minus.
Contd……
𝜀2 =
𝑜. 1 + 0.1 + 0.1
1.15
= 0.26
𝛼2 =
1
2
ln 1 − 0.26
0.26
= 0.52
Now calculating values Wn
(3) as
Here second hypothesis has misclassified 6th, 7th and 8th so they are
provided with higher weights as :
W6
(3)=W7
(3)= W8
(3)=0.1*e(0.52)=0.16
Whereas the data points 1,2,3,4,5,9,10 are properly classified so their
weights remains same as:
W1
(3)=W2
(3)=W9
(3)=W10
(3)= 0.1
W3
(3)=W4
(3)=W5
(3)=0.15
Cont….
Round 3:
Figure 5. Third hypothesis h3 misclassified 2 plus and 1 minus.
Contd…
Calculating error and learning terms for third
hypothesis:
𝜀3 =
0.1 + 0.1 + 0.1
1.33
= 0.21
𝛼3 =
1
2
ln
1 − 0.21
0.21
= 0.66
Contd…
Contd…..
Figure 6. Final hypothesis
Adaboost algorithm provides a strong classification
mechanism combining various weak classifiers resulting into
strong classifier which then is able to increase accuracy and
efficiency.
Final learner will have minimum error and maximum learning
rate resulting to the high degree of accuracy.
Hence, Adaboost algorithm can be used in such where
misclassification leads to dire consequences very successfully
at some extent.
Conclusions
[1]. Eric Bauer“An Empirical Comparison of Voting Classification Algorithms: Bagging,
Boosting, and Variants “, Computer Science Department, Stanford University Stanford CA,
94305, 1998.
[2]. K. Tumer and J. Ghosh, “Classifier Combining: Analytical Results and Implications,” Proc
Nat’l Conf. Artificial Intelligence , Portland,Ore.,1996.
[3]. Paul Viola and Michael Jones,” Fast and Robust Classification using Asymmetric AdaBoost
and a Detector Cascade”, Mistubishi Electric Research Lab Cambridge, MA.
[4]. P´adraig Cunningham, Matthieu Cord, and Sarah Jane Delany,” Machine learning
techniques for multiledia case studies on organization and retrival” Cord,M,
Cunningham,2008.
[5]. Trevor Hastie,” Multi-class AdaBoost” Department of Statistics Stanford University , CA
94305”,January 12, 2006.
[6]. Yanmin Sun, Mohamed S. Kamel and Yang Wang, “Boosting for Learning Multiple
Classes with Imbalanced Class Distribution”, The Sixth International Conference on Data
Mining (ICDM’06).
Refrences
Any queries..?
Any Questions?

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boosting algorithm

  • 1. Presentation on Boosting Approach For Classification Problems Presenter: Prithvi Raj Paneru M.Sc. CSIT(2013-15) Roll no: 1
  • 2. 1. Introduction 2. Combining Classifiers 3. Bagging 4. Boosting 5. AdaBoost Algorithm 6. Conclusion 10. References Overview
  • 3. Supervised learning is the machine learning task .  infer a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of a input object and a desired output value called a supervisory signal. Optimal scenario ? Target: generalize the learning algorithm from the training data to unseen situation in reasonable way. Introduction
  • 4.  Classification is a type of supervised learning.  Classification relies on a priori reference structures that divide the space of all possible data points into a set of classes that are usually, but not necessarily, non- overlapping.  A very familiar example is the email spam-catching system. Classification
  • 5.  The main issue in the classification is miss classification.  which leads to the under-fitting and over-fitting problems.  Like in the case of spam filtering due to miss classification the spam may be classified as not spam which is not considerable sometime.  So the major issue here to improve the accuracy of the classification. Contd……
  • 6. Combining classifiers makes the use of some weak classifiers and combining such classifier gives a strong classifier. Combining Classifiers
  • 8. Bagging (Bootstrap aggregating) operates using bootstrap sampling. Given a training data set D containing m examples, bootstrap sampling draws a sample of training examples, Di, by selecting m examples uniformly at random with replacement from D. The replacement means that examples may be repeated in Di. Bagging
  • 10. Training Phase Initialize the parameters D={Ф} h=the number of classification For k=1 to h Take a bootstrap sample Sk from training set S Build the classifier Dk using Sk as training set D=DUDi Return D Classification Phase Run D1,D2,………..Dk on the input k The class with maximum number of vote is choosen as the label for X. Bagging Algorithm
  • 11. Boosting has been a very successful technique for solving the two-class classification problem. It was first introduced by Freund & Schapire (1997), with their AdaBoost algorithm . Rather than just combining the isolated classifiers boosting use the mechanism of increasing the weights of misclassified data in preceding classifiers. A weak learner is defined to be a classifier which is only slightly correlated with the true classification. In contrast, a strong learner is a classifier that is arbitrarily well- correlated with the true classification. Boosting
  • 13. 1. Initialize the data weighting coefficients {Wn } by setting Wi = 1/n, for n=1,2……..,N 2. For m=1 to m a. Fit a classifier y 𝑚(x) to the training data by minimizing the weighted error function. b. Evaluate the quantities The term I(ym(xn)≠tn) is indication function has values 0/1, 0 if xn is properly classified 1 if not so. AdaBoost Algotithm
  • 14. And use these to evaluate c. Update the data weighting coefficients 3. Make predictions using the final model, which is given by Contd….
  • 15.  Let us take following points training set having 10 points represented by plus or minus.  Assumption is the original status is assign equal weight to all points.  Let us take following points training set having 10 points represented by plus or minus.  Assumption is the original status is assign equal weight to all points.  i.e. W1 (1) =W1 (2 ) =…………….=W1 (10)=1/10.  Figure1. Training set consisting 10 samples Example AdaBoost
  • 16. Round 1: Three “plus” points are not correctly classified. They are given higher weights. Figure 2. First hypothesis h1 misclassified 3 plus. Contd…..
  • 17. And error term and learning rate for first hypothesis as: 𝜖1 = 0.1+0.1+0.1 1 = 0.30 𝛼1 = 1 2 ln 1 − 0.30 0.30 = 0.42 Now we calculate the weights of each data points for second hypothesis as: Wn (m+1)=? 1st, 2nd, 6th, 7th, 8th, 9th and 10th data points are classified properly so their weight remains same. i.e. W1 (2)=W2 (2)=W6 (2)=W7 (2)=W8==W9 (2)=W10 (2)= 0.1 but 3rd,4th and 5th data points are misclassified so higher weights are provided to them as W3 (2)=W4 (2)=W5 (2)=0.1*e0.42=0.15 Contd..
  • 18. Round 2: Three “minuse” points are not correctly classified. They are given higher weights. Figure5. Second Hypothesis h2 misclassified 3 minus. Contd……
  • 19. 𝜀2 = 𝑜. 1 + 0.1 + 0.1 1.15 = 0.26 𝛼2 = 1 2 ln 1 − 0.26 0.26 = 0.52 Now calculating values Wn (3) as Here second hypothesis has misclassified 6th, 7th and 8th so they are provided with higher weights as : W6 (3)=W7 (3)= W8 (3)=0.1*e(0.52)=0.16 Whereas the data points 1,2,3,4,5,9,10 are properly classified so their weights remains same as: W1 (3)=W2 (3)=W9 (3)=W10 (3)= 0.1 W3 (3)=W4 (3)=W5 (3)=0.15 Cont….
  • 20. Round 3: Figure 5. Third hypothesis h3 misclassified 2 plus and 1 minus. Contd…
  • 21. Calculating error and learning terms for third hypothesis: 𝜀3 = 0.1 + 0.1 + 0.1 1.33 = 0.21 𝛼3 = 1 2 ln 1 − 0.21 0.21 = 0.66 Contd…
  • 23. Adaboost algorithm provides a strong classification mechanism combining various weak classifiers resulting into strong classifier which then is able to increase accuracy and efficiency. Final learner will have minimum error and maximum learning rate resulting to the high degree of accuracy. Hence, Adaboost algorithm can be used in such where misclassification leads to dire consequences very successfully at some extent. Conclusions
  • 24. [1]. Eric Bauer“An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants “, Computer Science Department, Stanford University Stanford CA, 94305, 1998. [2]. K. Tumer and J. Ghosh, “Classifier Combining: Analytical Results and Implications,” Proc Nat’l Conf. Artificial Intelligence , Portland,Ore.,1996. [3]. Paul Viola and Michael Jones,” Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade”, Mistubishi Electric Research Lab Cambridge, MA. [4]. P´adraig Cunningham, Matthieu Cord, and Sarah Jane Delany,” Machine learning techniques for multiledia case studies on organization and retrival” Cord,M, Cunningham,2008. [5]. Trevor Hastie,” Multi-class AdaBoost” Department of Statistics Stanford University , CA 94305”,January 12, 2006. [6]. Yanmin Sun, Mohamed S. Kamel and Yang Wang, “Boosting for Learning Multiple Classes with Imbalanced Class Distribution”, The Sixth International Conference on Data Mining (ICDM’06). Refrences