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ML - Candidate Elimination Algorithm - GeeksforGeeks
ML – Candidate Elimination Algorithm
Difficulty Level : Easy
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Last Updated : 17 Mar, 2021
The candidate elimination algorithm incrementally builds the version space given a
hypothesis space H and a set E of examples. The examples are added one by one;
each example possibly shrinks the version space by removing the hypotheses that
are inconsistent with the example. The candidate elimination algorithm does this by
updating the general and specific boundar y for each new example.
You can consider this as an extended form of Find-S algorithm.
Consider both positive and negative examples.
Actually, positive examples are used here as Find-S algorithm (Basically they are
generalizing from the specification).
While the negative example is specified from generalize form.
Terms Used:
Concept learning : Concept learning is basically learning task of the machine
(Learn by Train data)
General Hypothesis : Not Specif ying features to learn the machine.
G = {‘?’, ‘?’,’?’,’?’…}: Number of attributes
Specific Hypothesis : Specif ying features to learn machine (Specific feature)
S= {‘pi’,’pi’,’pi’…}: Number of pi depends on number of attributes.
Version Space : It is intermediate of general hypothesis and Specific hypothesis. It
not only just written one hypothesis but a set of all possible hypothesis based on
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training data-set.
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Algorithm:
Step1: Load Data set
Step2: Initialize General Hypothesis
Step3: For each training example
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and Specific
Hypothesis.
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Step4: If example is positive example
if attribute_value == hypothesis_value:
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else:
replace attribute value with '?' (Basically generalizi
Step5: If example is Negative example
Make generalize hypothesis more specific.
Example :
Consider the dataset given below:
Algorithmic steps :
Initially : G = [[?, ?, ?, ?, ?, ?], [?, ?, ?, ?, ?, ?], [?, ?, ?,
[?, ?, ?, ?, ?, ?], [?, ?, ?, ?, ?, ?], [?, ?, ?,
S = [Null, Null, Null, Null, Null, Null]
For instance 1 : <'sunny','warm','normal','strong','warm ','same'>
G1 = G
S1 = ['sunny','warm','normal','strong','warm ','same']
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For instance 2 : <'sunny','warm','high','strong','warm ','same'> an
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G2 = G
S2 = ['sunny','warm',?,'strong','warm ','same']
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For instance 3 : <'rainy','cold','high','strong','warm ','change'>
G3 = [['sunny', ?, ?, ?, ?, ?], [?, 'warm', ?, ?, ?, ?]
[?, ?, ?, ?, ?, ?], [?, ?, ?, ?, ?, ?], [?, ?, ?,
S3 = S2
For instance 4 : <'sunny','warm','high','strong','cool','change'> a
G4 = G3
S4 = ['sunny','warm',?,'strong', ?, ?]
At last, by synchronizing
the G4 and S4 algorithm produce the outp
Output :
G = [['sunny', ?, ?, ?, ?, ?], [?, 'warm', ?, ?, ?, ?]]
S = ['sunny','warm',?,'strong', ?, ?]
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Ar ticle Contributed By :
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@deepanshu_rustagi
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