mfe (version 0.1.5)

concept: Concept Meta-features

Description

Concept characterization features measure the sparsity of the input space and the irregularity of the input-output distribution measures extract information about validation index.

Usage

concept(...)
# S3 method for default
concept(
  x,
  y,
  features = "all",
  summary = c("mean", "sd"),
  transform = TRUE,
  ...
)
# S3 method for formula
concept(
  formula,
  data,
  features = "all",
  summary = c("mean", "sd"),
  transform = TRUE,
  ...
)

Arguments

...

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 "all" to include all them.

summary

A list of summarization functions or empty for all values. See post.processing method to more information. (Default: c("mean", "sd"))

transform

A logical value indicating if the categorical attributes should be transformed. If FALSE they will be ignored. (Default: TRUE)

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.

Value

A list named by the requested meta-features.

Details

The following features are allowed for this method:

"cohesiveness"

Example Cohesiveness is a different version of the wgDist measure.

"conceptvar"

Concept variation estimates the variability of class labels among examples.

"impconceptvar"

Improved concept variation is a different version of the conceptvar measure.

"wgDist"

Weighted distance captures how dense or sparse is the example distribution.

References

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.

See Also

Other meta-features: clustering(), complexity(), general(), infotheo(), itemset(), landmarking(), model.based(), relative(), statistical()

Examples

# NOT RUN {
## Extract all meta-features using formula
concept(Species ~ ., iris)

## Extract some meta-features
concept(iris[1:4], iris[5], c("conceptvar"))

## Use another summarization function
concept(Species ~ ., iris, summary=c("min", "median", "max"))
# }