Description Usage Arguments Details Value References See Also Examples
Concept characterization features measure the sparsity of the input space and the irregularity of the input-output distribution measures extract information about validation index.
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... |
Further arguments passed to the summarization functions. |
x |
A data.frame contained only the input attributes. |
y |
A factor response vector with one label for each row/component of x. |
features |
A list of features names or |
summary |
A list of summarization functions or empty for all values. See
post.processing method to more information. (Default:
|
transform |
A logical value indicating if the categorical attributes
should be transformed. If |
formula |
A formula to define the class column. |
data |
A data.frame dataset contained the input attributes and class. The details section describes the valid values for this group. |
The following features are allowed for this method:
Example Cohesiveness is a different version of the wgDist measure.
Concept variation estimates the variability of class labels among examples.
Improved concept variation is a different version of the conceptvar measure.
Weighted distance captures how dense or sparse is the example distribution.
A list named by the requested meta-features.
Vilalta, R., & Drissi, Y. (2002). A characterization of difficult problems in classification. In M. A. Wani, H. R. Arabnia, K. J. Cios, K. Hafeez, G. Kendall (Eds.), Proceedings ofthe 2002 international conference on machine learning and applications - ICMLA 2002, June 24-27, 2002, Las Vegas, Nevada (pp. 133-138).
Vilalta, R., 1999. Understanding accuracy performance through concept characterization and algorithm analysis. In: ECML Workshop on Recent Advances in Meta-Learning and Future Work. pp. 3-9.
Other meta-features:
clustering()
,
complexity()
,
general()
,
infotheo()
,
itemset()
,
landmarking()
,
model.based()
,
relative()
,
statistical()
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