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Comparing Performance of Decision Diagrams vs. Case Retrieval Nets

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Comparing Performance of Decision Diagrams vs. Case Retrieval Nets

  1. 1. Comparing Performance of Decision Diagrams and Case Retrieval Nets Alexandre Hanft & Matthias Ringe Intelligent Information Systems Lab, University of Hildesheim Alexandre.hanft|matthias.ringe@uni-hildesheim.de
  2. 2. FG-WM @ LWA 2008 | 2008-10-07 2 of 15 Outline • Application Domain • Decision diagrams • Comparison – General Comparison – Build-up – Retrieval • Future Work
  3. 3. FG-WM @ LWA 2008 | 2008-10-07 3 of 15 Application Domain: insurance claims • Pristinely 9500 cases: Atttributes: plausibilitaet (engl.: plausibility) [2] free text, ≥1 techn. device schadensursache (cause_of_loss) [7] • Consolidated: 18086 eingang_datum (date_of_receipt) [7], only year structured cases, 1 device zustand (condition) [9] zeitwert_bis (present_value_to) [13] • 13 Case Attributes typ (type) [18] – [amount of different schadensobjekt (claim_object) [34] values]: reparaturkosten_bis (cost_of_repair_to) [35] geraetealter_in_jahren (object_age) [41] • Similarity modelling: anschaffungswert_bis (acquisition_value_to) [102] functions for numerical zeitwert_von (present_value_from) [140] values, taxonomies reparaturkosten_von (cost_of_repair_from) [317] anschaffungswert_von (acquisition_value_from) [4730]
  4. 4. FG-WM @ LWA 2008 | 2008-10-07 4 of 15 Example case: 1 device of an insurance claim (consolidated) • 5 of 13 Case Attributes: plausib claim object acquisition No ility cause of loss object age value from – plausibilitaet (engl.: plausibility) [2] 1 true water damage computer 1 800 – schadensursache (cause_of_loss) [7] 2 true water damage computer 3 800 … – schadensobjekt 3 false water damage computer 4 650 (claim_object) [34] washing – geraetealter_in_jahren 4 true overvoltage machine 5 999 (object_age) [41] 5 false overvoltage dryer 6 550 – anschaffungswert_von (acquisition_value_from) 6 true lightning laptop 5 999 [4730] 7 false water damage laptop 4 1300 8 false water damage laptop 4 700
  5. 5. FG-WM @ LWA 2008 | 2008-10-07 5 of 15 Application Domain: insurance claims • 13 Case Attributes: – plausibilitaet (engl.: plausibility) [2] – schadensursache (cause_of_loss) [7] – eingang_datum (date_of_receipt) [7], only year – zustand (condition) [9] – zeitwert_bis (present_value_to) [13] – typ (type) [18] – schadensobjekt (claim_object) [34] – reparaturkosten_bis (cost_of_repair_to) [35] – geraetealter_in_jahren (object_age) [41] – anschaffungswert_bis (acquisition_value_to) [102] – zeitwert_von (present_value_from) [140] – reparaturkosten_von (cost_of_repair_from) [317] – anschaffungswert_von (acquisition_value_from) [4730]
  6. 6. FG-WM @ LWA 2008 | 2008-10-07 6 of 15 Decision Diagrams (DD) • Assumption: list of fixed attribute-value-pairs(AVP) • Directed graph, source, sink • Node labeled with Attribute (except sink) • Edge labeled with value • Case = path source … sink • [Nicholson et al., 2006] [Nicholson et al., 2006] R. Nicholson, D. Bridge, and N. Wilson. Decision diagrams: Fast and flexible support for case retrieval and recommendation. In Mehmet H. Göker, Thomas Roth-Berghofer, and H. Altay Güvenir, (eds.), Proceedings of the 8th ECCBR’06, Ölüudeniz/Fethiye, Turkey, volume 4106 of LNCS, pages 136–150, Heidelberg, 2006. Springer Verlag.
  7. 7. FG-WM @ LWA 2008 | 2008-10-07 7 of 15 Decision Diagrams: Example plausibility caus_of_loss claim_object object_age aquisition_value
  8. 8. FG-WM @ LWA 2008 | 2008-10-07 8 of 15 Retrieval in Decision Diagrams • Look for the path(case) with smallest distance (f in sink) α(n n’) = wα ∗ distα(v, v’) v=query value, v‘ value on edge ⎧ ⎪ 0, if n = source f (n) = def ⎨ ⎪min ( f (n' ) + α (n' → n)), else ⎩ n→n ' ⎧ ⎪ 0, if n = sink g (n) =def ⎨ ⎪min (α (n → n' ) + g (n' )), else ⎩ n→n '
  9. 9. FG-WM @ LWA 2008 | 2008-10-07 9 of 15 Retrieval in Decision Diagrams: Example
  10. 10. FG-WM @ LWA 2008 | 2008-10-07 10 of 15 Case Retrieval Nets (CRN) [optional] • attribute-value pair is Information Entity (IE) • case descriptor node • case is a sub- graph of the CRN • [Lenz 1999]
  11. 11. FG-WM @ LWA 2008 | 2008-10-07 11 of 15 Decision Diagrams (DDs) vs. Case Retrieval Nets (CRN) General Comparison Approach Decision Diagram Case Retrieval Net feature retrieval approach index-oriented index-oriented calculation of local similarities/ during retrieval during build-up distances dealing with NULL Values as normal values (otherwise can be omitted uncomplete paths) determination of similarities distances similar cases adding new cases during yes yes lifetime direct assignment of cases from not directly directly through ID in index structure to case base case descriptor suitability for incomplete cases no yes suitability for domain with high no no amount of different attribute values
  12. 12. FG-WM @ LWA 2008 | 2008-10-07 12 of 15 DD vs. CRN: Comparison procedure Selected Attribute: • Measurements processed for 5: plausibility, cause of loss, – For Build-up condition, type, claim object – For Retrieval 10: plausibility, cause of loss, condition, type, claim object, – For Insertion of 1 new case object age, present value to, – 5, 10 and all(13) attributes present value from, cost of • 1st-10th attribute: 725 different AVPs repair to, acquisition value to • 1st-13th attribute: 5.455 different AVPs 13: plausibility, cause of loss, condition, type, claim object, – 1.000, 2.000,...18.000 cases object age, present value to, – Average of last 5 from 6 runs present value from, cost of repair to, acquisition value to, • tests run on an ordinary PC cost of repair from, date of – 1.83GHz Core2 CPU, 2GB RAM, Win XP receipt, acquisition value from – Implemented in .net
  13. 13. FG-WM @ LWA 2008 | 2008-10-07 13 of 15 Compare Build-Up • with13 attributes • DD is always faster (65% time in ø) – 22.35 vs. 41.34sec • DD: sigmoidal – only add edges for different paths • CRN: parabola – add at least 13 relevance arcs + sims • Doesn’t rise exponentially
  14. 14. FG-WM @ LWA 2008 | 2008-10-07 14 of 15 Compare Retrieval in CRNs • Case: 5, 10, 13 Attributes • 5: 0.34 to 4.1 msec • 10: 0.61 to 5.2 msec • 13: 2 to 17.5 msec • Each curve: Nearly linear • Outliner: due to internal .net data structures
  15. 15. FG-WM @ LWA 2008 | 2008-10-07 15 of 15 Compare Retrieval in DDs with Taxonomies • Case: 5, 10, 13 Attributes • Increase linear up to 9000, afterwards constant • 0.33 to 2.55sec: Slow! – parser calls to calculate similarity in taxonomies – Omit in following test series
  16. 16. FG-WM @ LWA 2008 | 2008-10-07 16 of 15 Retrieval CRNs vs. DDs w/o taxonomies • CRN: 13: 2 to 17.5 msec • DD: 5.8 to 69 msec • CRN are always faster
  17. 17. FG-WM @ LWA 2008 | 2008-10-07 17 of 15 Compare Insertion of a case Einfügen in DD Einfügen in CRN 0,1600 0,0009 0,1400 0,0008 0,0007 0,1200 Laufzeit in Sekunden Laufzeit in Sekunden 0,0006 0,1000 0,0005 0,0800 0,0004 0,0600 0,0003 0,0400 0,0002 0,0200 0,0001 0,0000 0,0000 00 00 00 00 00 00 00 00 00 0 0 0 0 0 0 0 0 0 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 0 0 0 0 15 0 0 0 0 0 10 20 30 40 50 60 70 80 90 00 00 00 00 00 00 00 00 00 10 11 12 13 14 15 16 17 18 10 20 30 40 50 60 70 80 90 10 11 12 13 14 16 17 18 Anzahl Fälle Anzahl Fälle • Insert one case with 13 attributes • DD: 24.6 to 142.8 msec • CRN: around 0.7 msec: 30 to 183 times faster!
  18. 18. FG-WM @ LWA 2008 | 2008-10-07 18 of 15 Conclusion & Future Work • comparison Decision Diagrams with Case Retrieval Nets – build-up: DDs are faster (local similarities inserted in CRN) – Retrieval: CRNs are faster (local similarities exist in CRN) • In-depth investigation with same dataset as [Nicholson et al., 2006] • investigate dependency of the duration time for build-up and retrieval from the amount and distribution of the values of the attributes with artificial datasets.
  19. 19. Thank you for your attention! Questions | Suggestions | Comments

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