XCSF with Local Deletion:Preventing Detrimental Forgetting<br />Olivier Sigaud<br />Institut des Systèmes Intelligents et ...
Motivation<br />Achieve the following goals:<br />Maintain a complete solution<br />Avoid detrimental forgetting<br />Enab...
Observation<br />XCSF reproduces locally but deletes globally.<br />This is good, because we generate a generalization pre...
Approach:Choose local candidates for deletion without dependency on their generality.<br />Algorithm<br />Select random cl...
The Two Evaluation Functions<br />Crossed-Ridge Function<br />Diagonal Sine Function<br />
Evaluation with Different Sampling Types<br />Normal: Uniform Sampling<br />Random walk sampling:<br />Next sample is loca...
Crossed RidgeUniform Sampling<br />
Crossed-Ridge ComparisonBefore Condensation<br />Normal XCSF<br />XCSF with Local Deletion<br />
Crossed-Ridge ComparisonAfter Condensation<br />Normal XCSF	<br />XCSF with Local Deletion<br />
Crossed RidgeRandom Walk Sampling<br />
Crossed RidgeRing-based Gaussian Sampling<br />
Sine FunctionUniform Sampling<br />
Diagonal Sine FunctionBefore Condensation<br />Normal XCSF	<br />XCSF with Local Deletion<br />
Diagonal Sine FunctionAfter Condensation<br />Normal XCSF	<br />XCSF with Local Deletion<br />
Sine FunctionRandom Walk Sampling<br />
Sine FunctionRandom Walk Sampling in Ring<br />
Sine FunctionGaussian Sampling<br />
Sine FunctionRing-based Gaussian Sampling<br />
Summary & Conclusions<br />Local deletion does not negatively affect performance.<br />During condensation, local deletion...
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XCSF with Local Deletion: Preventing Detrimental Forgetting

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Martin V. Butz, Olivier Sigaud. "XCSF with Local Deletion: Preventing Detrimental Forgetting", IWLCS, 2011

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XCSF with Local Deletion: Preventing Detrimental Forgetting

  1. 1. XCSF with Local Deletion:Preventing Detrimental Forgetting<br />Olivier Sigaud<br />Institut des Systèmes Intelligents et de Robotique, Université Pierre et Marie Curie Paris 6. CNRS UMR 7222, 4 place Jussieu, F-75005 Paris, France<br />olivier.sigaud@upmc.fr<br />Martin V. Butz<br />Department of Psychology III<br />University of Würzburg<br />Röntgenring 11, 97070 Würzburg, Germany<br />butz@psychologie.uni-wuerzburg.de<br />
  2. 2. Motivation<br />Achieve the following goals:<br />Maintain a complete solution<br />Avoid detrimental forgetting<br />Enable continuous learning with selective focus<br />… particularly in problems where: <br />the problem space is non-uniformly or non-independently sampled (not iid).<br />the sub-space is not fully sampled (learning in manifolds).<br />some problem subspaces need to be known (smaller error) better than others (selective learning).<br />
  3. 3. Observation<br />XCSF reproduces locally but deletes globally.<br />This is good, because we generate a generalization pressure (local classifiers are on average more general). <br />This is bad, however, because non-uniformly sampled problems can lead to forgetting.<br />Thus, how can we<br />delete locally and still<br />generate the generalizationpressure?<br />
  4. 4. Approach:Choose local candidates for deletion without dependency on their generality.<br />Algorithm<br />Select random classifier cl from [M].<br />[D] = <br />for all c2[P] do<br /> if cl does match center of c then<br /> add c to candidate list [D]<br /> end if<br />end for<br />DELETE FROM CANDIDATE LIST [D]<br />
  5. 5. The Two Evaluation Functions<br />Crossed-Ridge Function<br />Diagonal Sine Function<br />
  6. 6. Evaluation with Different Sampling Types<br />Normal: Uniform Sampling<br />Random walk sampling:<br />Next sample is located in radial vicinity of previous one<br />Random walk sampling in ring (area of distance .3 to .4 of center)<br />Centered, Gaussian sampling <br />Ring-based Gaussian sampling<br />Parameter Settings: N = 4000, ²0= 0.002<br />
  7. 7. Crossed RidgeUniform Sampling<br />
  8. 8. Crossed-Ridge ComparisonBefore Condensation<br />Normal XCSF<br />XCSF with Local Deletion<br />
  9. 9. Crossed-Ridge ComparisonAfter Condensation<br />Normal XCSF <br />XCSF with Local Deletion<br />
  10. 10. Crossed RidgeRandom Walk Sampling<br />
  11. 11. Crossed RidgeRing-based Gaussian Sampling<br />
  12. 12. Sine FunctionUniform Sampling<br />
  13. 13. Diagonal Sine FunctionBefore Condensation<br />Normal XCSF <br />XCSF with Local Deletion<br />
  14. 14. Diagonal Sine FunctionAfter Condensation<br />Normal XCSF <br />XCSF with Local Deletion<br />
  15. 15. Sine FunctionRandom Walk Sampling<br />
  16. 16. Sine FunctionRandom Walk Sampling in Ring<br />
  17. 17. Sine FunctionGaussian Sampling<br />
  18. 18. Sine FunctionRing-based Gaussian Sampling<br />
  19. 19. Summary & Conclusions<br />Local deletion does not negatively affect performance.<br />During condensation, local deletion can assure a better problem solution sustenance.<br />Some of the results also indicate better structural development during learning.<br />These results have been confirmed in various other settings.<br />No apparent drawback to apply local deletion (constant overhead computationally)<br />Use this mechanism also in other condition settings!<br />Use it also to selectively learn higher accurate and lower accurate approximations in different problem subspaces!<br />

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