Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0304-3975(00)00274-7
Title: Predictive learning models for concept drift
Authors: Case, J.
Jain, S. 
Kaufmann, S.
Sharma, A.
Stephan, F.
Issue Date: 2001
Source: Case, J., Jain, S., Kaufmann, S., Sharma, A., Stephan, F. (2001). Predictive learning models for concept drift. Theoretical Computer Science 268 (2) : 323-349. ScholarBank@NUS Repository. https://doi.org/10.1016/S0304-3975(00)00274-7
Abstract: Concept drift means that the concept about which data is obtained may shift from time to time, each time after some minimum permanence. Except for this minimum permanence, the concept shifts may not have to satisfy any further requirements and may occur infinitely often. Within this work is studied to what extent it is still possible to predict or learn values for a data sequence produced by drifting concepts. Various ways to measure the quality of such predictions, including martingale betting strategies and density and frequency of correctness, are introduced and compared with one another. For each of these measures of prediction quality, for some interesting concrete classes, (nearly) optimal bounds on permanence for attaining learnability are established. The concrete classes, from which the drifting concepts are selected, include regular languages accepted by finite automata of bounded size, polynomials of bounded degree, and sequences defined by recurrence relations of bounded size. Some important, restricted cases of drifts are also studied, for example, the case where the intervals of permanence are computable. In the case where the concepts shift only among finitely many possibilities from certain infinite, arguably practical classes, the learning algorithms can be considerably improved. © 2001 Elsevier Science B.V. All rights reserved.
Source Title: Theoretical Computer Science
URI: http://scholarbank.nus.edu.sg/handle/10635/39920
ISSN: 03043975
DOI: 10.1016/S0304-3975(00)00274-7
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

17
checked on Dec 6, 2017

WEB OF SCIENCETM
Citations

15
checked on Nov 18, 2017

Page view(s)

65
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.