ordDataGen: Artificial data for testing ordEval algorithms

In CORElearn: Classification, Regression and Feature Evaluation

Artificial data for testing ordEval algorithms

Description

The generator produces ordinal data simulating different profiles of attributes: basic, performance, excitement and irrelevant.

Usage

  ordDataGen(noInst, classNoise=0)

Arguments

noInst	Number of instances to generate.
classNoise	Proportion of randomly determined values in the class variable.

Details

Problem is described by six important and two irrelevant features. The important features correspond to different feature types from the marketing theory: two basic features (B_{weak} and B_{strong}), two performance features (P_{weak} and P_{strong}), two excitement features (E_{weak} and E_{strong}), and two irrelevant features (I_{uniform} and I_{normal}). The values of all features are randomly generated integer values from 1 to 5, indicating for example score assigned to each of the features by the survey's respondent. The dependent variable for each instance (class) is the sum of its features' effects, which we scale to the uniform distribution of integers 1-5, indicating, for example, an overall score assigned by the respondent.

% C=b_w(B_{weak})+b_s(B_{strong})+p_w(P_{weak})+p_s(P_{strong})+e_w(E_{weak})+e_s(E_{strong})%

Value

The method returns a data.frame with noInst rows and 9 columns. Range of values of the attributes and class are integers in [1,5]

Author(s)

Marko Robnik-Sikonja

See Also

classDataGen, regDataGen, ordEval,

Examples

#prepare a data set
dat <- ordDataGen(200)

# evaluate ordered features with ordEval
est <- ordEval(class ~ ., dat, ordEvalNoRandomNormalizers=100)
# print(est)  
plot(est)

CORElearn documentation built on Nov. 18, 2022, 5:08 p.m.