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)
``` |

```
Loading required package: Rcpp
A set consisting of 36 rules:
1. IF hue is [-Inf,0.907] and magnesium is (102, Inf] and proline is (537,837] THEN is 3;
(supportSize=4; laplace=0.714285714285714)
2. IF od is [-Inf,2.47] and ash is (2.26,2.46] and alcohol is (12.5,13.5] and alcalinity_of_ash is (18.2,20.8] THEN is 3;
(supportSize=3; laplace=0.666666666666667)
3. IF malid_acid is (2.42, Inf] and od is [-Inf,2.47] THEN is 3;
(supportSize=20; laplace=0.91304347826087)
4. IF malid_acid is (2.42, Inf] and od is [-Inf,2.47] THEN is 3;
(supportSize=20; laplace=0.91304347826087)
5. IF color_intensity is (5.35, Inf] and hue is [-Inf,0.907] THEN is 3;
(supportSize=18; laplace=0.857142857142857)
6. IF proanthocyanins is [-Inf,1.36] and alcohol is (13.5, Inf] and malid_acid is (2.42, Inf] THEN is 3;
(supportSize=6; laplace=0.777777777777778)
7. IF flavanoids is [-Inf,1.59] and magnesium is (91,102] THEN is 3;
(supportSize=12; laplace=0.8)
8. IF proline is (537,837] and total_phenols is [-Inf,1.96] and od is [-Inf,2.47] THEN is 3;
(supportSize=13; laplace=0.8125)
9. IF proline is (537,837] and nonflavanoid_phenols is [-Inf,0.283] and proanthocyanins is [-Inf,1.36] THEN is 3;
(supportSize=3; laplace=0.666666666666667)
10. IF total_phenols is (1.96,2.56] and nonflavanoid_phenols is (0.4, Inf] and ash is [-Inf,2.26] THEN is 3;
(supportSize=1; laplace=0.5)
... and 26 other rules.
[1] 0.9577465
```

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