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=== Experiments with highly correlated data === | === Experiments with highly correlated data === | ||
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| − | [[Image:Hca_Old Kiwi.jpg|500px|left|thumb|Figure 2]] | + | [[Image:Hca_Old Kiwi.jpg|none|500px|left|thumb|Figure 2]] |
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=== Experiments with poorly correlated data === | === Experiments with poorly correlated data === | ||
Revision as of 22:11, 8 March 2008
Contents
Experiments and notes
Maybe you will think that the categorization accuracy of Bayes system goes down as the number of features goes up. The fact is that as we increase the size of the feature vector we have more elements to describe and categorize a class. Therefore, more features should not be bad. The issue is that if we add features with high correlation between classes, the information provided by those features is almost useless. The classes at those features are not separable, so we cannot make a good decision based on highly correlated features. Consequently, when we are characterizing classes by their features, we need to select features that have low correlation between the classes. Then, the information in the feature vector is meaningful for our categorization. The more meaningful features we have the more accurate the system will be.
The setup for the experiment should be the following. We only have two classes, Class 1 and Class 2. You can test your algorithms for different priori probabilities for Class 1 and Class 2. However if you are working with synthetic data, it is better to have equal priori probabilities to analyze the data. First, generate Gaussian data to train and test the system. We generated N = 105 samples. Also, the variance for each feature was the same and the mean of each class feature changed depending on the hypothesis we wanted to test. Then for each data set, compute the probability model parameters—mean and covariance matrix. Consequently, use Bayes decision formula to categorize the data. Kept track of the accuracy of the system for each feature vector size. The size of our feature vector was in the range of 1 to 20 features. Our measure of accuracy was the number of samples classified correctly divided by the total number of samples for the class (N = 105).
