ID3 algorithm (D).pdf

8/10/2019 ID3 algorithm (D).pdf

1/5

25/11/2014 ID3 algorithm - Wikipedia, the free encyclopedia

http://en.wikipedia.org/wiki/ID3_algorithm

ID3 algorithmFrom Wikipedia, the free encyclopedia

In decision tree learning, ID3(Iterative Dichotomiser 3) is an algorithm invented by Ross Quinlan[1]use

to generate a decision tree from a dataset. ID3 is the precursor to the C4.5 algorithm, and is typically used

in the machine learning and natural language processing domains.

Contents

1 Algorithm

1.1 Summary

1.2 Pseudocode

1.3 Properties

1.4 Usage

2 The ID3 metrics

2.1 Entropy

2.2 Information Gain

3 See also

4 References

5 External links

Algorithm

The ID3 algorithm begins with the original set as the root node. On each iteration of the algorithm, it

iterates through every unused attribute of the set and calculates the entropy (or information gain

) of that attribute. It then selects the attribute which has the smallest entropy (or largest informatio

gain) value. The set is then split by the selectedattribute (e.g. age < 50, 50 = 100) to

produce subsets of the data. The algorithm continues to recurse on each subset, considering only attributes

never selected before.

Recursion on a subset may stop in one of these cases:

every element in the subset belongs to the same class (+ or -), then the node is turned into a leaf and

labelled with the class of the examples

there are no more attributes to be selected, but the examples still do not belong to the same class

(some are + and some are -), then the node is turned into a leaf and labelled with the most common

class of the examples in the subset

there are no examples in the subset, this happens when no example in the parent set was found to be

matching a specific value of the selected attribute, for example if there was no example with age >=
http://en.wikipedia.org/wiki/Machine_learninghttp://en.wikipedia.org/wiki/Natural_language_processinghttp://en.wikipedia.org/wiki/Decision_tree_learninghttp://en.wikipedia.org/wiki/C4.5_algorithmhttp://en.wikipedia.org/wiki/Decision_tree_learninghttp://en.wikipedia.org/wiki/Decision_tree_learninghttp://en.wikipedia.org/wiki/Algorithmhttp://en.wikipedia.org/wiki/Decision_tree_learninghttp://en.wikipedia.org/wiki/C4.5_algorithmhttp://en.wikipedia.org/wiki/Ross_Quinlanhttp://en.wikipedia.org/wiki/Natural_language_processinghttp://en.wikipedia.org/wiki/Machine_learning


2/5



100. Then a leaf is created, and labelled with the most common class of the examples in the parent

set.

Throughout the algorithm, the decision tree is constructed with each non-terminal node representing the

selected attribute on which the data was split, and terminal nodes representing the class label of the final

subset of this branch.

Summary

1. Calculate the entropy of every attribute using the data set

2. Split the set into subsets using the attribute for which entropy is minimum (or, equivalently,

information gain is maximum)

3. Make a decision tree node containing that attribute

4. Recurse on subsets using remaining attributes.

Pseudocode

ID3 (Examples, Target_Attribute, Attributes)

Create a root node for the tree

If all examples are positive, Return the singlenode tree Root, with label = +.

If all examples are negative, Return the singlenode 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, , of A,

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

Let Examples( ) be the subset of examples that have the value for A

If Examples( ) 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( ), Target_Attribute, Attributes {A})

End

Return Root

Properties

ID3 does not guarantee an optimal solution it can get stuck in local optimums. It uses a greedy approach b

selecting the best attribute to split the dataset on each iteration. One improvement that can be made on the

algorithm can be to use backtracking during the search for the optimal decision tree.

ID3 can overfit to the training data, to avoid overfitting, smaller decision trees should be preferred over

larger ones. This algorithm usually produces small trees, but it does not always produce the smallest

possible tree.

ID3 is harder to use on continuous data. If the values of any given attribute is continuous, then there are

many more places to split the data on this attribute, and searching for the best value to split by can be time

consuming.

Usage
http://en.wikipedia.org/wiki/Overfittinghttp://en.wikipedia.org/wiki/Backtrackinghttp://en.wikipedia.org/wiki/Entropy_(information_theory)


3/5


4/5



In ID3, information gain can be calculated (instead of entropy) for each remaining attribute. The attribute

with the largestinformation gain is used to split the set on this iteration.

See also

CART

C4.5 algorithm

References

1. ^Quinlan, J. R. 1986. Induction of Decision Trees. Mach. Learn. 1, 1 (Mar. 1986), 81-106

Mitchell, Tom M.Machine Learning. McGraw-Hill, 1997. pp. 5558.

Grzymala-Busse, Jerzy W. "Selected Algorithms of Machine Learning from Examples."Fundament

Informaticae18, (1993): 193207.

External links

Seminars - http://www2.cs.uregina.ca/

(http://www2.cs.uregina.ca/~hamilton/courses/831/notes/ml/dtrees/4_dtrees1.html)

Description and examples - http://www.cise.ufl.edu/ (http://www.cise.ufl.edu/~ddd/cap6635/Fall-

97/Short-papers/2.htm)

Description and examples - http://www.cis.temple.edu/

(http://www.cis.temple.edu/~ingargio/cis587/readings/id3-c45.html)

An implementation of ID3 in Python

(http://www.onlamp.com/pub/a/python/2006/02/09/ai_decision_trees.html)

An implementation of ID3 in Ruby (http://ai4r.org/machineLearning.html)

An implementation of ID3 in Common Lisp (http://www.pvv.ntnu.no/~oyvinht/static/OSS/cl-id3/)

An implementation of ID3 algorithm in C# (http://www.codeproject.com/cs/algorithms/id3.asp)

An implementation of ID3 in Perl (https://metacpan.org/module/AI::DecisionTree)

An implementation of ID3 in Prolog (http://ftp.cs.stanford.edu/cs/robotics/shoham/prolog.tar.Z)

An implementation of ID3 in C (This code is commented by non-English language)

(http://id3alg.altervista.org)

Retrieved from "http://en.wikipedia.org/w/index.php?title=ID3_algorithm&oldid=633226059"

Categories: Decision trees Classification algorithms
http://www.onlamp.com/pub/a/python/2006/02/09/ai_decision_trees.htmlhttp://en.wikipedia.org/wiki/Classification_and_regression_treehttp://id3alg.altervista.org/http://en.wikipedia.org/wiki/Category:Decision_treeshttp://www.pvv.ntnu.no/~oyvinht/static/OSS/cl-id3/https://metacpan.org/module/AI::DecisionTreehttp://www.codeproject.com/cs/algorithms/id3.asphttp://en.wikipedia.org/wiki/C4.5_algorithmhttp://en.wikipedia.org/w/index.php?title=ID3_algorithm&oldid=633226059http://en.wikipedia.org/wiki/Entropy_(information_theory)http://en.wikipedia.org/wiki/Category:Classification_algorithmshttp://www.cis.temple.edu/~ingargio/cis587/readings/id3-c45.htmlhttp://www.cise.ufl.edu/~ddd/cap6635/Fall-97/Short-papers/2.htmhttp://en.wikipedia.org/wiki/Help:Categoryhttp://ai4r.org/machineLearning.htmlhttp://ftp.cs.stanford.edu/cs/robotics/shoham/prolog.tar.Zhttp://www2.cs.uregina.ca/~hamilton/courses/831/notes/ml/dtrees/4_dtrees1.html


5/5



This page was last modified on 10 November 2014 at 13:30.Text is available under the Creative Commons Attribution-ShareAlike License additional terms mayapply. By using this site, you agree to the Terms of Use and Privacy Policy. Wikipedia is aregistered trademark of the Wikimedia Foundation, Inc., a non-profit organization.
http://wikimediafoundation.org/wiki/Privacy_policyhttp://en.wikipedia.org/wiki/Wikipedia:Text_of_Creative_Commons_Attribution-ShareAlike_3.0_Unported_Licensehttp://wikimediafoundation.org/wiki/Terms_of_Usehttp://www.wikimediafoundation.org/

Documents

ID3 algorithm (D).pdf