Description Usage Arguments Value Author(s) References See Also Examples

View source: R/RuleInduction.R

A version of the AQ algorithm which was originally proposed by R.S. Michalski. This implamentation is based on a concept of a local (object-relative) decision reduct from RST.

1 | ```
RI.AQRules.RST(decision.table, confidence = 1, timesCovered = 1)
``` |

`decision.table` |
an object inheriting from the |

`confidence` |
a numeric value giving the minimal confidence of computed rules. |

`timesCovered` |
a positive integer. The algorithm will try to find a coverage of training examples with rules,
such that each example is covered by at least |

An object of a class `"RuleSetRST"`

. For details see `RI.indiscernibilityBasedRules.RST`

.

Andrzej Janusz

R.S. Michalski, K. Kaufman, J. Wnek: "The AQ Family of Learning Programs: A Review of Recent Developments and an Exemplary Application", Reports of Machine Learning and Inference Laboratory, George Mason University (1991)

`predict.RuleSetFRST`

, `RI.indiscernibilityBasedRules.RST`

, `RI.CN2Rules.RST`

,
`RI.LEM2Rules.RST`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ```
###########################################################
## Example
##############################################################
data(RoughSetData)
wine.data <- RoughSetData$wine.dt
set.seed(13)
wine.data <- wine.data[sample(nrow(wine.data)),]
## Split the data into a training set and a test set,
## 60% for training and 40% for testing:
idx <- round(0.6 * nrow(wine.data))
wine.tra <-SF.asDecisionTable(wine.data[1:idx,],
decision.attr = 14,
indx.nominal = 14)
wine.tst <- SF.asDecisionTable(wine.data[(idx+1):nrow(wine.data), -ncol(wine.data)])
true.classes <- wine.data[(idx+1):nrow(wine.data), ncol(wine.data)]
## discretization:
cut.values <- D.discretization.RST(wine.tra,
type.method = "unsupervised.quantiles",
nOfIntervals = 3)
data.tra <- SF.applyDecTable(wine.tra, cut.values)
data.tst <- SF.applyDecTable(wine.tst, cut.values)
## rule induction from the training set:
rules <- RI.AQRules.RST(data.tra, confidence = 0.9, timesCovered = 3)
rules
## predicitons for the test set:
pred.vals <- predict(rules, data.tst)
## checking the accuracy of predictions:
mean(pred.vals == true.classes)
``` |

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