CFS Feature Set Evaluation
Contact: Martin Gütlein
Categories: Feature selection
Exposed methods:
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Feature selection |
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|---|---|
| Input: | |
| Output: | |
| Input format: | Weka's ARFF format |
| Output format: | Weka's ARFF format |
| User-specified parameters: | |
| Reporting information: | |
Description:
CFS is a correlation-based filter method CFS from [Hal98]. It gives high scores to subsets that include features
that are highly correlated to the class attribute but have low correlation to each other Let S be an attribute
subset that has k attributes, rcf models the correlation of the attributes to the class attribute, rff the
intercorrelation between attributes.
meritS = k rcf / sqrt( k+k(k-1) rff )
Background (publication date, popularity/level of familiarity, rationale of approach, further comments)
Default Feature Set Evaluator in Weka. Advantage: fast filter method that can evaluate
sets (instead of single features only)
Class-blind/class-sensitive feature selection
Class-sensitive feature selection
Filter/wrapper/hybrid approach
Filter
Type of Descriptor:
Interfaces:
Priority: Medium
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
References:
[Hal98] Hall, M. A., Smith, L. A. (1998). Practical feature subset selection for machine learning. Australian Computer Science Conference. Springer. 181-191.
