📄 bayesclassifier.html
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<pre>Title: A Naive Bayesian Classifier in C#
Author: Erich Guenther
Email: erich_guenther@hotmail.com
Environment: Microsoft Visual C# 2005
Keywords: Algorithm, Bayes, Classification
Level: Intermediate"
Description: An article on Naive Bayesian Classifier in C#
Section Miscellaneous
SubSection General
</pre>
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<li ><a href="BayesClassifier/BayesClassifier.zip">Download source + demo project - 20 Kb </a></li>
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<p><img src="BayesClassifier/pict.gif" alt="Sample Image" ></p>
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<h2>Introduction</h2>
<p>
I was looking for a way to classify short texts into several categories. A simple
but probably sufficient method seemed to be naive bayesian classification. Looking
for some readily available stuff I found many different implementations in perl
or java. The only CLR implementation I could find was NClassifier, yet it was not
doing classification into multiple classes. Therefore I decided to write my own.
</p>
<h2>Background</h2>
<p>
There is plenty of information around in the net, describing the theory of bayesian
classification. <a href="http://en.wikipedia.org/wiki/Naive_Bayes_classifier">Wikipedia</a> has a good introduction.</p>
<h2>Using the code</h2>
<p>
First create an instance of <code>BayesClassifier.Classifier</code>.
</p><pre>BayesClassifier.Classifier m_Classifier = new BayesClassifier.Classifier();</pre><p>
Tip: you may experiment with <code>BayesClassifier.ExcludedWords</code> to define the words
that you will consider irrelevant for your classification. That can lead to smaller dictionaries
and therefore speed up the classification.</p>
<p>
Then define the categories and teach each cateory:
</p><pre>m_Classifier.TeachCategory("Cat1", new System.IO.StreamReader(file));
m_Classifier.TeachPhrases("Cat2", new string[] { "Hi", "HoHo" });
</pre><p>
Finally the method <code>BayesClassifier.Classifier.Classify</code> will return the classification result.
</p><pre>Dictionary<string, double> score = m_Classifier.Classify(new System.IO.StreamReader(file));
</pre>
<p>Let me know if you have any questions or suggestions, and let me know if you have any experiences with the applicability of the navie bayesian approach.(Since the
(wrong) assumption of word independence might turn out to be influence the result).</p>
<h2>History</h2>
<ul>
<li>28.May.2006 Version 1.0</li>
</ul>
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