Transcript The Impact of Feature Extraction on the Performance of a Classifier

AI’05 Victoria, British-Columbia, Canada May 9-11, 2005
The Impact of Feature Extraction on the
Performance of a Classifier:
kNN, Naïve Bayes and C4.5
Mykola Pechenizkiy
Department of Computer Science
and Information Systems
University of Jyväskylä
Finland
Contents
•
DM and KDD background
– KDD as a process
– DM strategy
•
Classification
– Curse of dimensionality and Indirectly relevant features
– Dimensionality reduction
• Feature Selection (FS)
• Feature Extraction (FE)
•
Feature Extraction for Classification
– Conventional PCA
– Random Rrojection
– Class-conditional FE: parametric and non-parametric
•
Experimental Results
– 4 FE methods, 3 Classifiers, 20 UCI datasets
•
Conclusions and Further Research
AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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What is Data Mining
Data mining or Knowledge discovery is the process of
finding previously unknown and potentially interesting
patterns and relations in large databases (Fayyad, KDD’96)
Data mining is the emerging science and industry of
applying modern statistical and computational
technologies to the problem of finding useful patterns
hidden within large databases (John 1997)
Intersection of many fields: statistics, AI, machine
learning, databases, neural networks, pattern recognition,
econometrics, etc.
AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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I
Knowledge discovery as a process
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.,
Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1997.
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The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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The task of classification
J classes, n training observations, p features
Given n training instances
Training
New instance
(xi, yi) where xi are values of
Set
to be classified
attributes and y is class
CLASSIFICATION
Goal: given new x0,
predict class y0
Examples:
Class Membership of
the new instance
- prognostics of recurrence of breast cancer;
- diagnosis of thyroid diseases;
- heart attack prediction, etc.
AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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Goals of Feature Extraction
Improvement of representation space
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The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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Selecting most
representative instances
pa
rti t
io
ns
Constructive Induction
representation of instances of class y1
representation of instances of class yk
Selecting most relevant features
•Feature extraction (FE) is a dimensionality reduction technique that
extracts a subset of new features from the original set by means of
some functional mapping keeping as much information in the data as
possible (Fukunaga 1990).
AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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Feature selection or transformation
• Features can be (and often are) correlated
– FS techniques that just assign weights to individual
features are insensitive to interacted or correlated
features.
• Data is often not homogenous
– For some problems a feature subset may be useful in one
part of the instance space, and at the same time it may be
useless or even misleading in another part of it.
– Therefore, it may be difficult or even impossible to remove
irrelevant and/or redundant features from a data set and
leave only useful ones by means of feature selection.
• That is why the transformation of the given representation
before weighting the features is often preferable.
AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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FE for Classification
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Principal Component Analysis
• PCA extracts a lower dimensional space
by analyzing the covariance structure of
multivariate statistical observations.
• The main idea – determine the features
that explain as much of the total variation
in the data as possible with as few of
these features as possible.
PCA has the following properties:
(1) it maximizes the variance of the extracted features;
(2) the extracted features are uncorrelated;
(3) it finds the best linear approximation;
(4) it maximizes the information contained in the extracted
features.
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The Computation of the PCA
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The Computation of the PCA
1) Calculate the covariance matrix S from the input data.
2) Compute the eigenvalues and eigenvectors of S and
sort them in a descending order with respect to the
eigenvalues.
3) Form the actual transition matrix by taking the
predefined number of components (eigenvectors).
4) Finally, multiply the original feature space with the
obtained transition matrix, which yields a lowerdimensional representation.
• The necessary cumulative percentage of variance
explained by the principal axes is used commonly as a
threshold, which defines the number of components to
be chosen.
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FT example “Heart Disease”
100%
Variance covered
87%
-0.7·Age+0.1·Sex-0.43·RestBP+0.57·MaxHeartRate
-0.01·Age+0.78·Sex-0.42·RestBP-0.47·MaxHeartRate
0.1·Age-0.6·Sex-0.73·RestBP-0.33·MaxHeartRate
60%
<= 3NN Accuracy =>
67%
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The Random Projection Approach
•
Dimensionality of data can be so high that commonly used FE
techniques like PCA are almost inapplicable because of extremely high
computational time/cost.
•
In RP a lower-dimensional projection is produced by means of
transformation like in PCA but the transformation matrix is generated
randomly (although often with certain constrains).
•
Johnson and Lindenstrauss Theorem: any set of n points in a ddimensional Euclidean space can be embedded into a k-dimensional
Euclidean space – where k is logarithmic in n and independent of d – so
that all pairwise distances are maintained within an arbitrarily small
factor
•
Achlioptas showed a very easy way of defining (and computing) the
transformation matrix for RP:
 1 with probabilit y 1 / 6

wij  3   0 with probabilit y 2 / 3
 1 with probabilit y 1 / 6

 1 with probabilit y 1 / 2
wij  
 1 with probabilit y 1 / 2
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The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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PCA for Classification
PCA gives high weights to features with higher variabilities
disregarding whether they are useful for classification or not.
x2
PC(2)
a)
PC(1)
x1
x2
PC(2)
b)
PC(1)
x1
PCA for classification: a) effective work of PCA, b) the case where an
irrelevant principal component was chosen from the classification point of
view.
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The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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Class-conditional Eigenvector-based FE
The usual decision is to use some class separability criterion,
based on a family of functions of scatter matrices: the withinclass, the between-class, and the total covariance matrices.
J (w ) 
wT S B w
w T SW w
Simultaneous Diagonalization Algorithm
•
Transformation of X to Y: Y  Λ 1/2ΦT X , where  and  are the
eigenvalues and eigenvectors matrices of SB.
•
Computation of SB in the obtained Y space.
•
Selection of m eigenvectors of SB, which correspond to the m largest
eigenvalues.
•
Computation of new feature space Z  ΨTm Y, where  is the set of
selected eigenvectors.
AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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Parametric Eigenvalue-based FE
The within-class covariance matrix shows the scatter of
samples around their respective class expected
vectors:
n
c
SW   ni  (x (ji )  m (i ) )( x (ji )  m (i ) )T
i
i 1
j 1
The between-class covariance matrix shows the
scatter of the expected vectors around the mixture
c
mean:
S B   ni (m (i )  m)(m (i )  m) T
i 1
where c is the number of classes, ni is the number of
(i )
instances in a class i, x j is the j-th instance of i-th
class, m(i) is the mean vector of the instances of i-th
class, and m is the mean vector of all the input data.
AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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Nonparametric Eigenvalue-based FE
Tries to increase the number of degrees of freedom in the between-class
covariance matrix, measuring the between-class covariances on a local
basis. K-nearest neighbor (kNN) technique is used for this purpose.
ni
c
c
S B  ni wik (x (ki )  m ik( j*) )(x (ki )  m ik( j*) )T
i 1 k 1
j 1
j i
  
The coefficient wik is a weighting coefficient, which shows importance of
each summand.
• assign more weight to those elements of the matrix, which involve instances
lying near the class boundaries and are more important for classification.
wik 
j)
min j {d  (x (ki ) , x (nNN
)}
c
(i )
( j)

d
(
x
,
x

nNN )
k
j 1
AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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Sb: Parametric vs Nonparametric
Differences in the between-class covariance matrix calculation for
nonparametric (left) and parametric (right)
approaches for the two-class case.
AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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Experimental Settings
•
•
20 data sets with different characteristics taken from the UCI
machine learning repository
3 classifiers: 3-nearest neighbor classification (3NN), Naïve-Bayes
(NB) learning algorithm, and C4.5 decision tree learning (C4.5)
– The classifiers were used from WEKA library with their defaults
settings.
•
4 FE techniques and case with no FE
– Random Projection (RP = A), PCA (B), parametric FE (PAR = C),
nonparametric FE (NPAR = D), no FE (Plain = E)
– For PCA and NPAR we used a 0.85 variance threshold, and for RP
we took the number of projected features equal to 75% of original
space. We took all the features extracted by parametric FE as it was
always equal to no._of_classes – 1.
•
•
30 test runs of Monte-Carlo cross validation were made for each
data set to evaluate the classification accuracy.
In each run, the training set/the test set = 70%/30% by stratified
random sampling to keep class distributions approximately same.
AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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Summary of Results (1)
•
For some data sets FE has no effect or deteriorates the classification
accuracy compared to plain case E.
– for 3NN: 9 data sets from 20:
• Breast, Diabetes, Glass, Heart, Iris, Led, Monk-3, Thyroid, and Tic.
– for NB: 6 data sets from 20:
• Diabetes, Heart, Iris, Lymph, Monk-3, and Zoo.
– for C4.5: 11 data sets from 20:
• Car, Glass, Heart, Ionosphere, Led, Led17, Monk-1, Monk-3, Vehicle, Voting,
and Zoo.
•
•
•
•
It can be seen also that often different FE techniques are the best for
different classifiers and for different data sets.
Class-conditional FE approaches, especially the nonparametric
approach are most often the best comparing to PCA or RP.
On the other hand it is necessary to point out that the parametric FE
was very often the worst, and for 3NN and C4.5 parametric FE was
the worst technique more often than RP. Such results highlight the
very unstable behavior of parametric FE.
Different FE techniques are often suited in different ways not only for
different data sets but also for different classifiers.
AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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Ranking of the FE techniques
according to the results on 20 UCI data sets
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The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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Summary of Results (2)
•
Basically, each bar on the histograms shows how many times an
FE technique was the 1st, the 2nd, the 3rd, the 4th, or the 5th
among the 20 possible. The number of times certain techniques
got 1st-5th place is not necessary integer since there were
draws between 2, 3, or 4 techniques. In such cases each
technique gets the ½, 1/3 or 1/4 score correspondingly.
•
It can be seen from the figure that there are many common
patterns in the behavior of techniques for 3 different classifiers,
yet there are some differences too. So, according the ranking
results RP behavior is very similar with every classifier, PCA
works better for C4.5, parametric FE is suited better for NB.
Nonparametric FE is also suited better for NB, it is also good
with 3NN. However, it is less successful for C4.5.
AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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Accuracy Changes due to the Use of FE
AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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Summary of Results (3)
•
The nonparametric approach is always the best on average for each
classifier, the second best is PCA, then parametric FE, and, finally,
RP shows the worst results.
•
Classification in the original space (Plain) was almost as good as in
the space of extracted features produced by the nonparametric
approach when kNN classifiers is used.
•
However, when NB is used, Plain accuracy is significantly lower
comparing to the situation when the nonparametric FE is applied. Still,
this accuracy is as good as in situation when PCA is applied and
significantly higher in situations when RP or the parametric FE is
applied.
•
For C4.5 the situation is also different. So, Plain classification is the
best option on average.
•
With respect to RP our results differ from the conclusions made in
(Fradkin and Madigan, 2003), where PR was found to be suited
better for nearest neighbor methods and less satisfactory for decision
trees (according to the results on 5 data sets).
AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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Conclusions and Further Research
•
Selection of FE method is not independent from the selection of
classifier
•
FE techniques are powerful tools that can significantly increase the
classification accuracy producing better representation spaces or resolving
the problem of “the curse of dimensionality”.
However, when applied blindly, FE may have no effect for the further
classification or even deteriorate the classification accuracy..
Our experimental results show that for many data sets FE does increase the
classification accuracy.
There is no best FE technique among the considered ones, and it is hard to
say which one is the best for a certain classifier and/or for a certain problem,
however according to the experimental results some preliminary trends can
be recognized.
– Class-conditional approaches (and especially nonparametric approach)
were often the best ones. This indicated the fact how important is to take
into account class information and do not rely only on the distribution of
variance in the data.
– At the same time it is important to notice that the parametric FE was
very often the worst, and for 3NN and C4.5 the parametric FE was the
worst more often than RP. Such results highlight the very unstable
behavior of parametric FE.
•
•
•
AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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Further Research (cont.)
•
•
•
•
One possibility to improve the parametric FE, we think, is to combine it with
PCA or a feature selection approach in a way that a few PCs or the most
useful for classification features are added to those extracted by the
parametric approach.
Although it is logical to assume that RP should have more success in
applications where the distances between the original data points are
meaningful and/or for such learning algorithms that use distances between the
data points, our results show that this is not necessary the rule.
Time taken to build classification models with and without FE and number of
features extracted by a certain FE technique are interesting issues to analyze.
A volume of accumulated empirical (and theoretical) findings, some trends,
and some dependencies with respect to data set characteristics and use of FE
techniques have been discovered and can be discovered.
–
•
Thus, potentially the adaptive selection of the most suitable data mining techniques
for a data set at consideration (that is a really challenging problem) might be
possible. We see our further research efforts in this direction.
Experiments on synthetically generated datasets:
–
generating, testing and validating hypothesis on DM strategy selection with respect to
a dataset at hand under controlled settings when some data characteristics are
varied while the others are held unchangeable.
AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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Contact Info
Mykola Pechenizkiy
Department of Computer Science and Information Systems,
University of Jyväskylä, FINLAND
E-mail: [email protected]
Tel. +358 14 2602472
Mobile: +358 44 3851845
Fax: +358 14 2603011
www.cs.jyu.fi/~mpechen
THANK YOU!
AI’05 Victoria, Canada, May 9-11, 2005
The Impact of FE on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 by Mykola Pechenizkiy
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