This programs is part of the WEKA classifier library. The code used to generate this program is from the java class 'weka/classifiers/rules/' from WEKA's libraries. The following description was taken from this classes JavaDoc information:

This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W. Cohen as an optimized version of IREP. The algorithm is briefly described as follows: Initialize RS = {}, and for each class from the less prevalent one to the more frequent one, DO: 1. Building stage: Repeat 1.1 and 1.2 until the descrition length (DL) of the ruleset and examples is 64 bits greater than the smallest DL met so far, or there are no positive examples, or the error rate >= 50%. 1.1. Grow phase: Grow one rule by greedily adding antecedents (or conditions) to the rule until the rule is perfect (i.e. 100% accurate). The procedure tries every possible value of each attribute and selects the condition with highest information gain: p(log(p/t)-log(P/T)). 1.2. Prune phase: Incrementally prune each rule and allow the pruning of any final sequences of the antecedents;The pruning metric is (p-n)/(p+n) -- but it's actually 2p/(p+n) -1, so in this implementation we simply use p/(p+n) (actually (p+1)/(p+n+2), thus if p+n is 0, it's 0.5). 2. Optimization stage: after generating the initial ruleset {Ri}, generate and prune two variants of each rule Ri from randomized data using procedure 1.1 and 1.2. But one variant is generated from an empty rule while the other is generated by greedily adding antecedents to the original rule. Moreover, the pruning metric used here is (TP+TN)/(P+N).Then the smallest possible DL for each variant and the original rule is computed. The variant with the minimal DL is selected as the final representative of Ri in the ruleset.After all the rules in {Ri} have been examined and if there are still residual positives, more rules are generated based on the residual positives using Building Stage again. 3. Delete the rules from the ruleset that would increase the DL of the whole ruleset if it were in it. and add resultant ruleset to RS. ENDDO Note that there seem to be 2 bugs in the original ripper program that would affect the ruleset size and accuracy slightly. This implementation avoids these bugs and thus is a little bit different from Cohen's original implementation. Even after fixing the bugs, since the order of classes with the same frequency is not defined in ripper, there still seems to be some trivial difference between this implementation and the original ripper, especially for audiology data in UCI repository, where there are lots of classes of few instances. Details please see: William W. Cohen: Fast Effective Rule Induction. In: Twelfth International Conference on Machine Learning, 115-123, 1995. PS. We have compared this implementation with the original ripper implementation in aspects of accuracy, ruleset size and running time on both artificial data "ab+bcd+defg" and UCI datasets. In all these aspects it seems to be quite comparable to the original ripper implementation. However, we didn't consider memory consumption optimization in this implementation.

NOTE: This algorithm has no parameter tuning, it is using the default WEKA parameters

NOTE: WEKA's Classifiers read a data in the .arff format. For Multiclass datasets, the SVMlight format converted to .arff multiclass format so they can be read by WEKA programs
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Existing runs on JRip_weka_nominal 1-25 of 125 < > Action_refresh_blue
ID Program Dataset Tuned hyper. User Updated << Status Total time Memory Error
Run #42947 JRip_weka_nominal projectTALEP no JeanC83 1y76d ago failed 37m42s 611M
Run #42827 JRip_weka_nominal projectTALEP_All no JeanC83 1y77d ago failed 1h44m 1524M
Run #41763 JRip_weka_nominal ProductClassys no chuertas 1y141d ago failed 1d0h 418M
Run #39769 JRip_weka_nominal GoodStudents no chuertas 1y285d ago failed 5h0m 418M
Run #39454 JRip_weka_nominal Los tres mosqueteros no chuertas 1y336d ago done 1m39s 420M 0.046
Run #39065 JRip_weka_nominal Ot Products no chuertas 2y8d ago failed 3h0m 420M
Run #38025 JRip_weka_nominal Axon Cars d50 t200 no chuertas 2y78d ago done 36m45s 421M 0.880
Run #37808 JRip_weka_nominal Plancarta r16.0 no chuertas 2y96d ago failed 32m44s 1077M
Run #37143 JRip_weka_nominal HealthyS12AsthmaS12 no zeros no OlavRG 2y156d ago done 2m23s 428M 0.524
Run #36568 JRip_weka_nominal EvoBio 15.0x+N no chuertas 2y223d ago done 39s 419M 0.200
Run #36544 JRip_weka_nominal EvoBio 14.0x+N no chuertas 2y223d ago done 1m0s 418M 0.138
Run #36486 JRip_weka_nominal EvoBio 11.0x+N no chuertas 2y224d ago done 1m8s 418M 0.200
Run #36404 JRip_weka_nominal EvoBio FULL no chuertas 2y229d ago failed 17m4s 1523M
Run #36362 JRip_weka_nominal EvoBio 10.0x+N no chuertas 2y230d ago done 1m28s 418M 0.185
Run #35530 JRip_weka_nominal ro-lts no chuertas 2y321d ago failed 1h0m 418M
Run #30181 JRip_weka_nominal copt test binary class no emrah 3y156d ago done 5m55s 433M 0.091
Run #30132 JRip_weka_nominal copt_1_mark no emrah 3y158d ago done 18m4s 420M 0.825
Run #29332 JRip_weka_nominal fml_project_bin no gtledward 3y311d ago done 11s 423M 0.457
Run #29168 JRip_weka_nominal fml_project2 no gtledward 3y314d ago done 11s 418M 0.510
Run #28856 JRip_weka_nominal Sca All Direction no andresvila 3y344d ago done 8s 426M 0.008
Run #28857 JRip_weka_nominal Sca Mov Direction no andresvila 3y344d ago done 2s 413M 0
Run #28705 JRip_weka_nominal Sca Motion no andresvila 3y344d ago done 18s 424M 0.011
Run #28448 JRip_weka_nominal NORMALIZED DATA no GregSen 4y20d ago done 5s 420M 0.260
Run #26990 JRip_weka_nominal zhangjun no blazebird 4y244d ago failed 12m29s 427M
Run #21362 JRip_weka_nominal ralign-random no internal 4y264d ago done 0.127

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