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classi cation for the classi ed plan and the underlying plan present in the training dataset. Clearly, the resulting classi cations are the same when the min t-norm is used. This technique has been shown to produce highly competitive results [51] in terms of both classi cation accuracy and number of rules generated. However, as is the case for most rule induction algorithms, the resultant rules may be unnecessarily complex due to the presence of redundant or misleading features. Fuzzy-rough feature selection (FRFS; see chapter 8) may be used to signi cantly reduce dataset dimensionality, removing redundant features that would otherwise increase rule complexity and reducing the time for the induction process itself. Applying this feature selection method to the example dataset removes feature b, and results in the set of rules given in Figure 3.1 upon application of SBA: From this ruleset, it can be seen that rule 2 has been simpli ed because of the redundancy of feature b. Although the extent of simpli cation is small in this case, with larger datasets the effect can be expected to be greater. The results using the FRFS-reduced dataset are provided in Table 3.4. The differences between the classi cations of the reduced and unreduced approaches have been highlighted (objects 4 and 7). In object 4 only the membership degree for Y has changed. This value has increased from 0.7 to 0.8, resulting in an ambiguous classi cation. Again, for case 7 the membership degree for Y is the only value to have changed; this time it more closely resembles the classi cation present in the training dataset.

$1/$3[E=STATU S:$2]

Substituted URL: /foo/bar/

/r {user | group}

Rule 1: IF a is A1, THEN Plan is X Rule 2: IF c is C2, THEN Plan is Y Rule 3: IF MF(Rule1) < AND MF(Rule2) < , THEN Plan is Z

When request for http://hostname/foo/s=java/bar/ is detected the Apache server assigns $1 to foo, $2 to java, and $3 to bar as shown in the Figure 9-5 The value for $2 is used to set environment variable STATUS and the Web server performs an internal redirect to the /foo/bar location The URL rewriting steps shown in Figure 9-5 can be implemented using this rewrite rule:

^(*)/S=([^/]+)/(*) $1/$3 [E=STATUS:$2]

Syntax for {user | group} must be expressed in one of the following ways: user@domain or domain\user group@domain or domain\group

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TABLE 3.4 Classi ed Plan with Reduced Features and the Actual Plan Object X 1 2 3 4 5 6 7 8 9 0.3 1.0 0.0 0.8 0.5 0.0 1.0 0.1 0.3 Classi ed Y 0.7 0.3 0.4 0.8 1.0 1.0 0.3 0.3 0.0 Z 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 X 0.1 0.8 0.0 0.6 0.6 0.0 0.7 0.0 0.0 Actual Y 0.9 0.2 0.2 0.3 0.8 0.7 0.4 0.0 0.0 Z 0.0 0.0 0.8 0.1 0.0 0.3 0.0 1.0 1.0

Here the value of $2 is stored in the environment variable called STATUS using the E flag When this rule is in place and a request such as lynx -dump -head http:// localhost/dir1/S=value/kabirhtml is made, the rewrite log (if enabled) will show:

(2) (3) (2) (5) (2) (2) (1) init rewrite engine with requested uri /dir1/S=value/kabirhtml applying pattern ^(*)/S=([^/]+)/(*) to uri /dir1/S=value/kabirhtml rewrite /dir1/S=value/kabirhtml -> /dir1/kabirhtml setting env variable STATUS to value local path result: /dir1/kabirhtml prefixed with document_root to /usr/local/apache/htdocs/dir1/kabirhtml go-ahead with /usr/local/apache/htdocs/dir1/kabirhtml [OK]

Syntax for permissions must be expressed in the following form:

I have shortened the rewrite log output for brevity Notice that mod_rewrite has set the STATUS variable to value , so now if a Perl-based CGI script wanted to access the value of the STATUS environment variable, it can use $ENV{STATUS} Similarly, a Server-Side Include (SSI) directive can also access this environment variable

3.2.2.2 WSBA In an attempt to tackle the limitations of the SBA method, the weighted subsethood-based approach was developed [286]. For this method no threshold values are required in the operation of the induction or classi cation algorithm. Additionally default rules are avoided. WSBA uses the subsethood values calculated in SBA to attach weights to the constituent parts of fuzzy complete rules. Fuzzy complete rules take the form:

Let s say that you have a few friends who want Web sites on your server Instead of giving them the http://wwwdomaincom/~username-type site, you decide to create http://wwwusernamedomaincom-type sites for each of them Naturally, you add each username-based host name (for example, wwwkabirdomaincom) in your DNS using a simple CNAME record that points to your Web server For example, in your DNS you might have:

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