CN2 algorithm - Revision history

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2020-02-13T03:52:13Z

← Previous revision		Revision as of 03:52, 13 February 2020
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	==External links==		==External links==
	* [~~http~~://~~www~~.~~springerlink~~.~~com/content/k6q2v76736w5039r/~~ CN2 Algorithm Description]		* [https://doi.org/10.1023%2FA%3A1022641700528 CN2 Algorithm Description]

	[[Category:Machine learning algorithms]]		[[Category:Machine learning algorithms]]

Mindmatrix: Reverted edits by 112.196.77.202 (talk) to last version by Frasiernator

2016-09-08T15:09:36Z

Reverted edits by 112.196.77.202 (talk) to last version by Frasiernator

← Previous revision		Revision as of 15:09, 8 September 2016
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	==Description of algorithm==		==Description of algorithm==
	The algorithm must be given a set of ~~exatuityyt480uyerr34534mples~~, TrainingSet, which have already been classified in order to generate a list of classification rules. A set of conditions, SimpleConditionSet, which can be applied, alone or in combination, to any set of examples is predefined to be used for the classification.		The algorithm must be given a set of examples, TrainingSet, which have already been classified in order to generate a list of classification rules. A set of conditions, SimpleConditionSet, which can be applied, alone or in combination, to any set of examples is predefined to be used for the classification.

	routine ~~Cutoitu34N2~~(TrainingSet)		routine CN2(TrainingSet)
	let the ClassificationRuleList be empty		let the ClassificationRuleList be empty
	repeat		repeat
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	'if ' the BestConditionExpression ' then the class is ' the MostCommonClass		'if ' the BestConditionExpression ' then the class is ' the MostCommonClass
	until the TrainingSet is empty or the BestConditionExpression is nil		until the TrainingSet is empty or the BestConditionExpression is nil
	return the ~~ClassificationRuleLetje9tye80ry78fjh534534ist~~		return the ClassificationRuleList

	routine Find_BestConditionExpression(TrainingSet)		routine Find_BestConditionExpression(TrainingSet)
	let the ~~ConditionalExrty8oty97etyhyer79pressionSet~~ be empty		let the ConditionalExpressionSet be empty
	let the BestConditionExpression be nil		let the BestConditionExpression be nil
	repeat		repeat

112.196.77.202: /* Description of algorithm */

2016-09-05T05:16:35Z

Description of algorithm

← Previous revision		Revision as of 05:16, 5 September 2016
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	==Description of algorithm==		==Description of algorithm==
	The algorithm must be given a set of ~~examples~~, TrainingSet, which have already been classified in order to generate a list of classification rules. A set of conditions, SimpleConditionSet, which can be applied, alone or in combination, to any set of examples is predefined to be used for the classification.		The algorithm must be given a set of exatuityyt480uyerr34534mples, TrainingSet, which have already been classified in order to generate a list of classification rules. A set of conditions, SimpleConditionSet, which can be applied, alone or in combination, to any set of examples is predefined to be used for the classification.

	routine ~~CN2~~(TrainingSet)		routine Cutoitu34N2(TrainingSet)
	let the ClassificationRuleList be empty		let the ClassificationRuleList be empty
	repeat		repeat
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	'if ' the BestConditionExpression ' then the class is ' the MostCommonClass		'if ' the BestConditionExpression ' then the class is ' the MostCommonClass
	until the TrainingSet is empty or the BestConditionExpression is nil		until the TrainingSet is empty or the BestConditionExpression is nil
	return the ~~ClassificationRuleList~~		return the ClassificationRuleLetje9tye80ry78fjh534534ist

	routine Find_BestConditionExpression(TrainingSet)		routine Find_BestConditionExpression(TrainingSet)
	let the ~~ConditionalExpressionSet~~ be empty		let the ConditionalExrty8oty97etyhyer79pressionSet be empty
	let the BestConditionExpression be nil		let the BestConditionExpression be nil
	repeat		repeat

Frasiernator at 16:01, 8 August 2014

2014-08-08T16:01:18Z

← Previous revision		Revision as of 16:01, 8 August 2014
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	{{Refimprove\|date=September 2012}}		{{Refimprove\|date=September 2012}}
	The '''CN2 induction algorithm''' <ref>Clark, P. and Niblett, T (1989) The CN2 induction algorithm. Machine Learning 3(4):261-283.</ref> ~~is a [[Algorithmic learning theory\|learning algorithm]] for [[rule induction]].~~ It is designed to work even when the training data is imperfect. It is based on ideas from the [[AQ algorithm]] and the [[ID3 algorithm]]. As a consequence it creates a rule set like that created by AQ but is able to handle noisy data like ID3.		The '''CN2 induction algorithm''' is a [[Algorithmic learning theory\|learning algorithm]] for [[rule induction]].<ref>Clark, P. and Niblett, T (1989) The CN2 induction algorithm. Machine Learning 3(4):261-283.</ref> It is designed to work even when the training data is imperfect. It is based on ideas from the [[AQ algorithm]] and the [[ID3 algorithm]]. As a consequence it creates a rule set like that created by AQ but is able to handle noisy data like ID3.

	==Description of algorithm==		==Description of algorithm==

2A01:110:10:3006:616B:4E92:1A22:92F0: Fixed year of citation

2014-03-10T15:37:15Z

Fixed year of citation

← Previous revision		Revision as of 15:37, 10 March 2014
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	{{Refimprove\|date=September 2012}}		{{Refimprove\|date=September 2012}}
	The '''CN2 induction algorithm''' <ref>Clark, P. and Niblett, T (~~1987~~) The CN2 induction algorithm. Machine Learning 3(4):261-283.</ref> is a [[Algorithmic learning theory\|learning algorithm]] for [[rule induction]]. It is designed to work even when the training data is imperfect. It is based on ideas from the [[AQ algorithm]] and the [[ID3 algorithm]]. As a consequence it creates a rule set like that created by AQ but is able to handle noisy data like ID3.		The '''CN2 induction algorithm''' <ref>Clark, P. and Niblett, T (1989) The CN2 induction algorithm. Machine Learning 3(4):261-283.</ref> is a [[Algorithmic learning theory\|learning algorithm]] for [[rule induction]]. It is designed to work even when the training data is imperfect. It is based on ideas from the [[AQ algorithm]] and the [[ID3 algorithm]]. As a consequence it creates a rule set like that created by AQ but is able to handle noisy data like ID3.

	==Description of algorithm==		==Description of algorithm==

Magioladitis: Remove unicode control characters (CHECKWIKI error 16) using AWB (8514)

2012-11-01T19:31:32Z

Remove unicode control characters (CHECKWIKI error 16) using AWB (8514)

← Previous revision		Revision as of 19:31, 1 November 2012
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	return the BestConditionExpression		return the BestConditionExpression

	== References ==		==References==
	<references />		<references />

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	{{~~AI~~-stub}}		{{AI-stub}}

BattyBot: changed {{Unreferenced}} to {{Refimprove}} & general fixes using AWB (8399)

2012-09-16T06:42:14Z

changed {{Unreferenced}} to {{Refimprove}} & general fixes using AWB (8399)

← Previous revision		Revision as of 06:42, 16 September 2012
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	{{~~Unreferenced~~\|date=~~October~~ ~~2011~~}}		{{Refimprove\|date=September 2012}}
	The '''CN2 induction algorithm''' <ref>Clark, P. and Niblett, T (1987) The CN2 induction algorithm. Machine Learning 3(4):261-283.</ref> is a [[Algorithmic learning theory\|learning algorithm]] for [[rule induction]]. It is designed to work even when the training data is imperfect. It is based on ideas from the [[AQ algorithm]] and the [[ID3 algorithm]]. As a consequence it creates a rule set like that created by AQ but is able to handle noisy data like ID3.		The '''CN2 induction algorithm''' <ref>Clark, P. and Niblett, T (1987) The CN2 induction algorithm. Machine Learning 3(4):261-283.</ref> is a [[Algorithmic learning theory\|learning algorithm]] for [[rule induction]]. It is designed to work even when the training data is imperfect. It is based on ideas from the [[AQ algorithm]] and the [[ID3 algorithm]]. As a consequence it creates a rule set like that created by AQ but is able to handle noisy data like ID3.

Blaz.zupan: reference added

2012-09-06T12:08:38Z

reference added

← Previous revision		Revision as of 12:08, 6 September 2012
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	{{Unreferenced\|date=October 2011}}		{{Unreferenced\|date=October 2011}}
	The '''CN2 induction algorithm''' is a [[Algorithmic learning theory\|learning algorithm]] for [[rule induction]]. It is designed to work even when the training data is imperfect. It is based on ideas from the [[AQ algorithm]] and the [[ID3 algorithm]]. As a consequence it creates a rule set like that created by AQ but is able to handle noisy data like ID3.		The '''CN2 induction algorithm''' <ref>Clark, P. and Niblett, T (1987) The CN2 induction algorithm. Machine Learning 3(4):261-283.</ref> is a [[Algorithmic learning theory\|learning algorithm]] for [[rule induction]]. It is designed to work even when the training data is imperfect. It is based on ideas from the [[AQ algorithm]] and the [[ID3 algorithm]]. As a consequence it creates a rule set like that created by AQ but is able to handle noisy data like ID3.

	==Description of algorithm==		==Description of algorithm==
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	== References ==		== References ==
	<references />		<references />
	* Clark, P. and Niblett, T (1987) The CN2 induction algorithm. Machine Learning 3(4):261-283.

	==External links==		==External links==

Blaz.zupan: /* References */

2012-09-06T10:36:58Z

References

← Previous revision		Revision as of 10:36, 6 September 2012
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	<references />		<references />
	* Clark, P. and Niblett, T (1987) The CN2 induction algorithm. Machine Learning 3(4):261-283.		* Clark, P. and Niblett, T (1987) The CN2 induction algorithm. Machine Learning 3(4):261-283.

	Mitchell, Tom M. ''Machine Learning''. McGraw-Hill, 1997. pp. 55-58.

	==External links==		==External links==

Blaz.zupan: /* External links */

2012-09-06T10:36:39Z

External links

← Previous revision		Revision as of 10:36, 6 September 2012
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	until the ConditionalExpressionSet is empty		until the ConditionalExpressionSet is empty
	return the BestConditionExpression		return the BestConditionExpression

			== References ==
			<references />
			* Clark, P. and Niblett, T (1987) The CN2 induction algorithm. Machine Learning 3(4):261-283.

			Mitchell, Tom M. ''Machine Learning''. McGraw-Hill, 1997. pp. 55-58.

	==External links==		==External links==