Learning in the presence of concept drift and hidden contexts
Citations
2,374 citations
Cites background or methods from "Learning in the presence of concept..."
...…Size [Gama et al. 2004], [Zhang et al. 2008], [Kuncheva and Zliobaite 2009] Forgetting Mechanisms Temporal Sequences [Salganicoff 1997], [Widmer and Kubat 1996], Abrupt Forgetting [Forman 2006], [Klinkenberg 2004], [Pechenizkiy are computed as Si = G(Xi, αSi−1), where α ∈ (0, 1) is the…...
[...]
...The FLORA2 algorithm [Widmer and Kubat 1996] includes a window adjustment heuristic for a rule-based classifier....
[...]
...…and Hulten 2000], [Kuncheva and Plumpton 2008], [Kelly et al. 1999] [Bouchachia 2011a], [Ikonomovska et al. 2011] [Salganicoff 1997], [Widmer and Kubat 1996], Fixed Size [Syed et al. 1999], [Hulten et al. 2001], [Lazarescu et al. 2004], Multiple Examples [Bifet and Gavalda 2006,…...
[...]
...One of the first algorithms using an adaptive window size was the FLORA2 [Widmer and Kubat 1996]....
[...]
...A typical section strategy monitors the evolution of the performance indicators [Widmer and Kubat 1996; Zeira et al. 2004] or raw data and statistically compares them to a fixed baseline....
[...]
1,790 citations
1,634 citations
Cites background from "Learning in the presence of concept..."
...Concept drift (Widmer and Kubat, 1996) in RL has not been directly addressed by any work in this survey....
[...]
1,621 citations
Cites background from "Learning in the presence of concept..."
...A more sensitive approach is required, which can make better distinctions between transient effects and long term patterns....
[...]
1,399 citations
Cites background from "Learning in the presence of concept..."
...For detailed treatment and some solutions see Widmer and Kubat (1996); Gama et al. (2004)....
[...]
References
5,311 citations
4,499 citations
"Learning in the presence of concept..." refers background or methods in this paper
...The more sophisticated variant IB3 (Aha et al., 1991) possesses a mech-anism similar to FLORA4 's for deciding which of the exemplars are `trust-worthy' predictors, which of them should be discarded as possibly noisy oroutdated, and which are as yet undecided....
[...]
...This general approach to deciding which hypotheses to trust has beenadopted from the instance-based learning method IB3 (Aha et al, 1991), whichalso uses statistical con dence measures to distinguish between reliable andunreliable predictors (exemplars in IB3 )....
[...]
...Simple Instance-Based Learning (sometimes calledmemory-based learning)algorithms like IB1 (Aha et al., 1991) can be viewed as incremental on-linelearners that rst classify each newly arrived example by some nearest-neighbor27 method and then store it as a new exemplar....
[...]
...Con dence intervals are computed as in (Aha et al., 1991)....
[...]
2,824 citations
1,967 citations
"Learning in the presence of concept..." refers background in this paper
...Again, this result assumes a minimum (fixed) window size of w(e,~) = m(e,~/2) where m(e,~) is derived from the general bound on the number of training examples that guarantee PAC-learning ( Blumer et al., 1989 ): re(e, 6) = maz( 4 log 3,-~ log !~)....
[...]
...In the following statement of results, the ci's are positive constants, e is the maximum allowed probability of misclassifying the next incoming example, n is the number of available attributes, and d is the Vapnik-Chervonenkis dimension (see, e.g., Blumer et al., 1989 ) of the target class....
[...]
...Again, this result assumes a mini-mum ( xed) window size which in this case turns out to be w( ; ) = m( ; =2)where m( ; ) is derived from the general bound on the number of train-ing examples that guarantee PAC-learning (Blumer et al., 1989): m( ; ) =max(4 log 2 ; 8d log 13 ) (where d is the VC-dimension of the class of target con-cepts)....
[...]
...…window size which in this case turns out to be w( ; ) = m( ; =2)where m( ; ) is derived from the general bound on the number of train-ing examples that guarantee PAC-learning (Blumer et al., 1989): m( ; ) =max(4 log 2 ; 8d log 13 ) (where d is the VC-dimension of the class of target con-cepts)....
[...]
1,797 citations