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RIPPER Fast Effective Rule Induction

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1 RIPPER Fast Effective Rule Induction
Machine Learning 2003 Merlin Holzapfel & Martin Schmidt

2 Rule Sets - advantages easy to understand
usually better than decision Tree learners representable in first order logic > easy to implement in Prolog prior knowledge can be added

3 Rule Sets - disadvantages
scale poorly with training set size problems with noisy data likely in real-world data goal: develop rule learner that is efficient on noisy data competitive with C4.5 / C4.5rules

4 Problem with Overfitting
overfitting also handles noisy cases underfitting is too general solution pruning: reduced error pruning (REP) post pruning pre pruning

5 Post Pruning (C4.5) overfit & simplify bottom - up
construct tree that overfits convert tree to rules prune every rule separately sort rules according accuracy consider order when classifying bottom - up

6 Pre pruning some examples are ignored during concept generation
final concept does not classify all training data correctly can be implemented in form of stopping criteria

7 Reduced Error Pruning seperate and conquer
split data in training and validation set construct overfitting tree until pruning reduces accuracy evaluate impact on validation set of pruning a rule remove rule so it improves accuracy most

8 Time Complexity REP has a time complexity of O(n4)
initial phase of overfitting alone has a complexity of O(n²) alternative concept Grow: faster in benchmarks time complexity still O(n4) with noisy data

9 Incremental Reduced Error Pruning - IREP
by Fürnkranz & Widmer (1994) competitive error rates faster than REP and Grow

10 How IREP Works iterative application of REP random split of sets
 bad split has negative influence (but not as bad as with REP) immediately pruning after a rule is grown (top-down approach)  no overfitting

11 Cohens IREP Implementation
build rules until new rule results in too large error rate divide data (randomly) into growing set(2/3) and pruning set(1/3) grow rule from growing set immediately prune rule Delete final sequence of conditions delete condition that maximizes function v until no deletion improves value of v add pruned rule to ruleset delete every example covered by rule (p/n)

12 Cohens IREP - Algorithm

13 IREP and Multiple Classes
order classes according to increasing prevalence (C1,....,Ck) find rule set to separate C1 from other classes IREP(PosData=C1,NegData=C2,...,Ck) remove all instances learned by rule set find rule set to separate C2 from C3,...,Ck ... Ck remains as default class

14 IREP and Missing Attributes
handle missing attributes: for all tests involving A if attribute A of an instance is missing test fails

15 Differences Cohen <> Original
pruning: final sequence <> single final condition stopping condition: error rate 50% <> accuracy(rule) < accuracy(empty rule) application: missing attributes, numerical variables, multiple classes <> two-class problems

16 Time Complexity IREP: O(m log² m), m = number of examples
(fixed number of classification noise)

17 37 Benchmark Problems

18 Generalization Performance
IREP performs worse on benchmark problems than C4.5rules won-lost-tie ratio: error ratio 1.13 excluding mushroom 1.52 including mushroom

19 Improving IREP three modifications:
alternative metric in pruning phase new stopping heuristics for rule adding post pruning of whole rule set (non-incremental pruning)

20 the Rule-Value Metric old metric not intuitive
R1: p1 = 2000, n1 = 1000 R2: p1 = 1000, n1 = 1 metric preferes R1 (fixed P,N) leads to occasional failure to converge new metric (IREP*)

21 Stopping Condition 50%-heuristics often stops too soon with moderate sized examples sensitive to the ‘small disjunct problem‘ solution: after a rule is added, the total description length of rule set and missclassifications (DL=C+E) If DL is d bits larger then the smallest length so far stop (min(DL)+d<DLcurrent) d = 64 in Cohen‘s implementation  MDL (Minimal Description Length) heuristics

22 IREP* IREP* is IREP, improved by the new rule-value metric and the new stopping condition against IREP against C4.5rules error ratio 1.06 (IREP 1.13) respectively 1.04 (1.52) including mushrooms

23 Rule Optimization post prunes rules produced by IREP*
The rules are considered in turn for each rule R, two alternatives are constructed Ri‘ new rule Ri‘‘ based on Ri final rule is chosen according to MDL

24 RIPPER IREP* is used to obtain a rule set
rule optimization takes place IREP* is used to cover remaining positive examples  Repeated Incremental Pruning to Produce Error Reduction

25 RIPPERk apply steps 2 and 3 k times

26 RIPPER Performance against IREP*

27 Error Rates RIPPER obviously is competitive

28 Efficency of RIPPERk modifications do not change complexity

29 Reasons for Efficiency
find model with IREP* and then improve effiecient first model with right size optimization takes linear time C4.5 has expensive optimization improvement process to large initial model RIPPER is especially more efficient on large noisy datasets

30 Conclusions IREP improved to IREP* IREP* improved to RIPPER
IREP is efficient rule learner for large noisy datasets but performs worse than C4.5 IREP improved to IREP* IREP* improved to RIPPER k iterated RIPPER is RIPPERk RIPPERk more efficient and performs better than C4.5

31 References Fast Effective Rule Induction William W. Cohen [1995]
Incremental Reduced Error Pruning J. Fürnkranz & G. Widmer [1994] Efficient Pruning Methods William W. Cohen [1993]

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