FOIL: Use FOIL to learn a rule set for classification

In arulesCBA: Classification Based on Association Rules

Use FOIL to learn a rule set for classification

Description

Build a classifier rule base using FOIL (First Order Inductive Learner), a greedy algorithm that learns rules to distinguish positive from negative examples.

Usage

FOIL(
  formula,
  data,
  max_len = 3,
  min_gain = 0.7,
  best_k = 5,
  disc.method = "mdlp"
)

Arguments

formula	A symbolic description of the model to be fitted. Has to be of form class ~ . or class ~ predictor1 + predictor2.
data	A data.frame or arules::transactions containing the training data. Data frames are automatically discretized and converted to transactions with prepareTransactions().
max_len	maximal length of the LHS of the created rules.
min_gain	minimal gain required to expand a rule.
best_k	use the average expected accuracy (laplace) of the best k rules per class for prediction.
disc.method	Discretization method used to discretize continuous variables if data is a data.frame (default: "mdlp"). See discretizeDF.supervised() for more supervised discretization methods.

Details

Implements FOIL (Quinlan and Cameron-Jones, 1995) to learn rules and then use them as a classifier following Xiaoxin and Han (2003).

For each class, we find the positive and negative examples and learn the rules using FOIL. Then the rules for all classes are combined and sorted by Laplace accuracy on the training data.

Following Xiaoxin and Han (2003), we classify new examples by

1. select all the rules whose bodies are satisfied by the example;

2. from the rules select the best k rules per class (highest expected Laplace accuracy);

3. average the expected Laplace accuracy per class and choose the class with the highest average.

Value

Returns an object of class CBA representing the trained classifier.

Author(s)

Michael Hahsler

References

Quinlan, J.R., Cameron-Jones, R.M. Induction of logic programs: FOIL and related systems. NGCO 13, 287-312 (1995). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/BF03037228")}

Yin, Xiaoxin and Jiawei Han. CPAR: Classification based on Predictive Association Rules, SDM, 2003. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1137/1.9781611972733.40")}

See Also

Other classifiers: CBA(), CBA_helpers, CBA_ruleset(), LUCS_KDD_CBA, RCAR(), RWeka_CBA

Examples

data("iris")

# learn a classifier using automatic default discretization
classifier <- FOIL(Species ~ ., data = iris)
classifier

# inspect the rule base
inspect(classifier$rules)

# make predictions for the first few instances of iris
predict(classifier, head(iris))

arulesCBA documentation built on May 29, 2024, 11:49 a.m.