101 lines
2.9 KiB
Plaintext
101 lines
2.9 KiB
Plaintext
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+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+
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SPAM e-mail database (spambase): Machine learning using strongly-typed GP
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with Open BEAGLE
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Copyright (C) 2001-2003
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by Christian Gagne <cgagne@gmail.com>
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and Marc Parizeau <parizeau@gel.ulaval.ca>
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+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+
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Getting started
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===============
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Example is compiled in binary 'spambase'. Usage options is described by
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executing it with command-line argument '-OBusage'. The detailed help can
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also be obtained with argument '-OBhelp'.
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Objective
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=========
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Find a program the will successfully predict whether a given e-mail is spam
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or not from some extracted features.
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Comments
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========
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The evolved programs works on floating-point values AND Booleans values.
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The programs must return a Boolean value which must be true if e-mail is
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spam, and false otherwise. Don't expect too much from this program as
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it is quite basic and not oriented toward performance. It is there mainly
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to illustrate the use of strongly-typed GP with Open BEAGLE.
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Terminal set
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============
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IN0, IN1, ... up to IN56, the e-mail features. [floating-point]
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0 and 1, two Boolean constants. [Boolean]
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Ephemeral constants randomly generated in $[0,100]$ [floating-point]
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Function set
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============
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AND [Inputs: Booleans, Output: Boolean]
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OR [Input: Boolean, Output: Boolean]
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NOT [Inputs: Booleans, Output: Boolean]
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+ [Inputs: floating-points, Output: floating-point]
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- [Inputs: floating-points, Output: floating-point]
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* [Inputs: floating-points, Output: floating-point]
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/ [Inputs: floating-points, Output: floating-point]
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< [Inputs: floating-points, Output: Booleans]
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== [Inputs: floating-points, Output: Booleans]
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if-then-else [1st Input: Boolean, 2nd & 3rd Input: floating-points,
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Output: floating-point]
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Fitness cases
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=============
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A random sample of 400 e-mails over the database, re-chosen for
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each fitness evaluation.
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Hits
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====
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Number of correct outputs obtained over the 400 fitness cases.
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Raw fitness
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===========
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Ignored (always 0).
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Standardized fitness
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====================
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Rate of correct outputs over the fitness cases where
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the desired output was 0 (non-spam).
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Adjusted fitness
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================
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Rate of correct outputs over the fitness cases where
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the desired output was 1 (spam).
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Normalized fitness
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==================
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Rate of correct outputs obtained over all the 400 fitness cases.
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Stopping criteria
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=================
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When the best individual scores 400 hits or when the evolution reaches
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the maximum number of generations.
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Reference
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=========
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Machine learning repository,
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http://www.ics.uci.edu/~mlearn/MLRepository.html
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