XCSF with Local Deletion: Preventing Detrimental Forgetting
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 />firstname.lastname@example.org<br />Martin V. Butz<br />Department of Psychology III<br />University of Würzburg<br />Röntgenring 11, 97070 Würzburg, Germany<br />email@example.com<br />
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 />
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 />
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 />
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 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 />
Sine FunctionRing-based Gaussian Sampling<br />
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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