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Information Gain Attribute Evaluation

Contact: Stefan Kramer

Categories: Feature selection

Exposed methods:

Feature selection

Input: Instances, feature vectors, class values
Output: Instances, feature vectors, class values
Input format: Weka's ARFF format
Output format: Weka's ARFF format
User-specified parameters: Number of features to select (non-mandatory) Information Gain Threshold (non-mandatory)
Reporting information: Attributes ranked by Information Gain

Description:

InfoGainAttributeEval evaluates the worth of an attribute by measuring the information gain with respect to the
class.
InfoGain(Class,Attribute) = H(Class) – H(Class | Attribute), where H is the information entropy.

Background (publication date, popularity/level of familiarity, rationale of approach, further comments)
Widely used standard feature selection method, disadvantage: does not take into
account feature interaction

Class-blind/class-sensitive feature selection
Class-sensitive feature selection

Type (optimal, greedy, randomized)
Optimal

Filter/wrapper/hybrid approach
Filter

Type of Descriptor:

Interfaces:

Priority: High

Development status:

Homepage:

Dependencies:
External components: WEKA


Technical details

Data: No

Software: Yes

Programming language(s): Java

Operating system(s): Linux, Win, Mac OS

Input format: Weka's ARFF format

Output format: Weka's ARFF format

License: GPL


References

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