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\begin{center} {\bf
Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern
Classification 
}
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{\LARGE 
J. Bala, J. Huang, H. Vafaie, K. DeJong, H. Wechsler \\ 
George Mason University
}
\vspace*{2cm}
{\Large 
Presented by \\
Sushil J. Louis \\
Department of Computer Science \\
University of Nevada \\
Reno, NV \\
sushil@cs.unr.edu \\
\verb+http://gaslab.cs.unr.edu+ \\
\verb+http://www.cs.unr.edu/~sushil+ \\
}
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\centerline{\fbox{Outline}}
	\bi
	\item Hybrid Approaches 
	\item Genetic Algorithms
	\item Decision Trees
	\item Feature selection
	\item Facial image data and Satellite images
	\item Results
	\ei
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\centerline{\underline{Pattern Classification}}
\bi
\item Difficult but fundamental task
\item Depends heavily on the particular choice of features used by classifier
	\bi
	\item[] Rank features according to some statistic

	\item[] Eliminate low-ranked features

	\ei
\item[$\leadsto$] Ranking is not enough: 
	\bi
	\item[] Non-linear interactions between features
	\item[] Does not measure effectiveness on the complete task
	\ei
\ei
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\centerline{\underline{Feature Selection}}
\bi 
\item First step in any classification task
\item[]
\item Feature Selection is crucial for classification performance
\item[]
\item Reduces amount of data
\item[]
\item Increases classification accuracy
\item[]
\item Very difficult: Number of features is very large, space of possible
subsets is exponential in number of features ($100 \mbox{features} \rightarrow
2^{100} \mbox{subsets}$).
\ei	
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\centerline{\underline{Optimal Feature Selection}}
\bi 
\item Minimize number of features
\item Improve classification rates
\item[]
\item GA + DT system: GA searches the space of all possible subsets of a large
set of candidate discrimination features for an optimal feature subset 
	\be
	\item For a given subset, ID3 produces a DT 
	\item Accuracy on unseen data $\rightarrow$ fitness to GA
	\item GA evolves better subsets
	\item[]
	\item[] Stop when performance passes a threshold
	\ee
\ei	
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\centerline{\underline{System Architecture}}
\bi
\item Initial (large) feature set
\item Run GA+ID3
\item Recommend best feature set
\ei
\vspace*{2ex}
\centerline{\mbox{\psfig{figure=figs/system.ps,height=3in,width=8in}}}

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\centerline{\underline{GA Representation}}
\bi
\item[]
\item Fixed length binary string
\item[]
\item $i^{th}$ position == 1 $\rightarrow$ $i^{th}$ feature is included.
\item[]
\item Genetic Algorithm modifies a population of such strings.
\item For each individual in the population
	\bi
	\item Run ID3 on training set containing only these features
	\item Evaluate resulting DT on test data
	\item Return accuracy to GA as fitness of individual
	\ei
\ei
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\centerline{\underline{Methodology}}

\centerline{\mbox{\psfig{figure=figs/method.ps,height=7in,width=6in}}}
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\centerline{\underline{Methodology (cont'd)}}
\bi
\item Standard GA, popsize 50, standard parameter values
\item C4.5, standard parameter values
\item 2-fold Cross-validation
\item 40\% training 60\% testing
\item 10 runs of the system with different seeds
\item Best tree out of ten is applied to testing data
\item Compare 
	\be
	\item Full feature set
	\item Same number of features but use ranking
	\ee
\item Satellite data (36 features total)
\item Facial data (105 features total)
\ei
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\centerline{\underline{{\bf Satellite Data}}}
\bi
\item Predict central pixel classification based on 3x3 neighborhood
multi-spectral values
\item Nasa Landsat images
\item 6 classes
\item 4 spectral bands, 9 pixel values $\rightarrow$ 36 features
\item Feature/pixel values are $0 - 255$
\item Learning/training set = $4435$ examples, test data = $2000$ examples
\ei
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\centerline{\underline{{\bf Results}}}

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\centerline{\underline{{\bf Facial Data}}}
\bi
\item Detecting eyes  very important for face recognition
\item 102 eyes, 102 nose, 102 other facial region examples for training
\item 52 eyes, 52 nose, 52 other facial region for testing
\item 105 features for each example
%\item Learning set of 102 examples and test set of 52 examples
\item Feature values between $0 - 255$
\item Three classes
\ei
\centerline{\mbox{\psfig{figure=figs/faceData.ps,height=3in,width=7in}}}
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\centerline{\underline{Results}}
\bi 
\item Better error rates
\item Reduced tree complexity for Satellite
\item 60\% reduction in facial features, 52\% in satellite
\ei
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\centerline{\underline{Results}}
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\centerline{\underline{Conclusions and Future Work}}
\bi 
\item These preliminary results are encouraging
\item Larger more complex problems in image processing
\item Use information other than classification accuracy as GA fitness
\ei
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