Information About

Id3 Algorithm
  CATEGORIES ABOUT ID3 ALGORITHM
   
trees structure
   
machine learning
   
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knowledge discovery in databases
   
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The algorithm is based on . Occam's razor is formalized using the concept of Information Entropy :

: I_{E}(i) = - \sum^{m}_{j=1} f (i,j) \log f (i, j)

The ID3 algorithm can be summarized as follows:

# Take all unused attributes and count their entropy concerning test samples
# Choose attribute for which entropy is minimum
# Make node containing that attribute

An explanation of the implementation of ID3 can be found at C4.5 Algorithm , which is an extended version of ID3.


ALGORITHM

The actual algorithm is as follows:

ID3 (Examples, Target_Attribute, Attributes)
  • Create a root node for the tree

  • If all examples are positive, Return the single-node tree Root, with label = +.

  • If all examples are negative, Return the single-node tree Root, with label = -.

  • If number of predicting attributes is empty, then Return the single node tree Root, with label = most common value of the target attribute in the examples.

  • Otherwise Begin

  • --- A = The Attribute that best classifies examples.

  • --- Decision Tree attribute for Root = A.

  • --- For each possible value, v_i, of A,

  • -- Add a new tree branch below Root, corresponding to the test A = v_i.

  • -- Let Examples(v_i), be the subset of examples that have the value v_i for A

  • -- If Examples(v_i) is empty

  • - Then below this new branch add a leaf node with label = most common target value in the examples

  • -- Else below this new branch add the subtree ID3 (Examples(v_i), Target_Attribute, Attributes – {A})

  • End

  • Return Root



EXTERNAL LINKS




SEE ALSO



REFERENCES


  • Mitchell, Tom M. ''Machine Learning''. McGraw-Hill, 1997.