mfe (version 0.1.5)

itemset: Itemset Meta-features

Description

Itemset characterization features measure measure the distribution of values of both single attributes and pairs of attributes.

Usage

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

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"))

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:

"oneitemset"

Individual frequency of each attributes' value.

"twoitemset"

Correlation information of the two attributes' value pairs.

"clssitemset"

It is a two itemset computed using a predictive attribute and the target.

References

Song, Q., Wang, G., & Wang, C. (2012). Automatic recommendation of classification algorithms based on data set characteristics. Pattern Recognition, 45(7), 2672-2689.

Wang, G., Song, Q., & Zhu, X. (2015). An improved data characterization method and its application in classification algorithm recommendation. Applied Intelligence, 43(4), 892-912.

See Also

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

Examples

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

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

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