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This section rst provides the results for the FRFS-based approach compared with the unreduced approach. Next a comparative experimental study is carried out between various dimensionality reduction methods; namely FRFS, entropy-based feature selection, PCA, and a random reduction technique. The experiments were carried out over a tolerance range (with regard to the employment of the RIA) in steps of 0.01. As mentioned earlier, a suitable value for the threshold must be chosen before rule induction can take place. However, the selection of tends to be an application-speci c task; a good choice for this threshold that provides a balance between a resultant ruleset s complexity and accuracy can be found by experiment. It should be noted that due to the fuzzy rule induction method chosen, all approaches generate exactly the same number of rules (as the number of classes of interest), but the arities in different rulesets differ. This way a potential complexity factor in the comparative studies can be avoided as otherwise the sizes of learned rulesets would need to be considered. Only the complexity in each learned rule is therefore examined,

The major difference between the Up-to-date and High Watermark vectors is that the High Watermark vector maintains values for domain controllers from which it requests changes, while the Up-to-date vector is maintained for every domain controller that has ever issued an originating update.

### Section 1: Global Environment ServerRoot /usr/local/apache PidFile logs/httpdpid <IfModule !perchildc> ScoreBoardFile logs/apache_runtime_status </IfModule> Timeout 300 KeepAlive On MaxKeepAliveRequests 100 KeepAliveTimeout 15 <IfModule preforkc> StartServers 5 MinSpareServers 5 MaxSpareServers 10 MaxClients 20 MaxRequestsPerChild 0 </IfModule> <IfModule threadedc> StartServers 3 MaxClients 8 MinSpareThreads 5 MaxSpareThreads 10 ThreadsPerChild 25 MaxRequestsPerChild 0 </IfModule> <IfModule perchildc> NumServers 5 StartThreads 5 MinSpareThreads 5 MaxSpareThreads 10 MaxThreadsPerChild 20 MaxRequestsPerChild 0 </IfModule> ### Section 2: Main server configuration Port 80 User nobody Group #-1

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ServerAdmin you@youraddress # Added ServerName wwwdomaincom DocumentRoot /usr/local/apache/htdocs <Directory /> Options FollowSymLinks AllowOverride None </Directory> <Directory /usr/local/apache/htdocs > Options Indexes FollowSymLinks MultiViews AllowOverride None Order allow,deny Allow from all </Directory> UserDir public_html DirectoryIndex indexhtml AccessFileName htaccess <Files ~ ^\ht > Order allow,deny Deny from all </Files> UseCanonicalName On TypesConfig conf/mimetypes DefaultType text/plain <IfModule mod_mime_magicc> MIMEMagicFile conf/magic </IfModule> HostnameLookups Off ErrorLog logs/error_log LogLevel warn LogFormat %h %l %u %t \ %r\ %>s %b \ %{Referer}i\ \ %{UserAgent}i\ combined LogFormat %h %l %u %t \ %r\ %>s %b common LogFormat %{Referer}i -> %U referer LogFormat %{User-agent}i agent CustomLog logs/access_log common ServerSignature On Alias /icons/ /usr/local/apache/icons/ <Directory /usr/local/apache/icons > Options Indexes MultiViews AllowOverride None

First of all, it is important to show that at least the use of features selected does not signi cantly reduce the classi cation accuracy as compared to the use of the full set of original features. For the 2-class problem the fuzzy-rough set-based feature selector returns 10 features out of the original 38. Figure 12.3 compares the classi cation accuracies of the reduced and unreduced datasets on both the training and testing data. As can be seen, the FRFS results are almost always better than the unreduced accuracies over the tolerance range. The best results for FRFS were obtained when is in the range 0.86 to 0.90, producing a classi cation accuracy of 83.3% on the training set and 83.9% for the test data. Compare this FRFS result with the optimum for the unreduced approach, which gave an accuracy of 78.5% for the training data and 83.9% for the test data.

As you can see, the Up-to -date and High Watermark vectors are complementary and are used to filter replication data so the unnecessary replication traffic does not occur.

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