Book Chapter10.1007/978-3-540-25966-4_1
Classifier Ensembles for Changing Environments
Ludmila I. Kuncheva
- 09 Jun 2004
- pp 1-15
388
TL;DR: This work considers strategies for building classifier ensembles for non-stationary environments where the classification task changes during the operation of the ensemble and the concept of ”forgetting” is discussed.
read more
Abstract: We consider strategies for building classifier ensembles for non-stationary environments where the classification task changes during the operation of the ensemble. Individual classifier models capable of online learning are reviewed. The concept of ”forgetting” is discussed. Online ensembles and strategies suitable for changing environments are summarized.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A survey on concept drift adaptation
TL;DR: The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art and aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.
Ensemble based systems in decision making
TL;DR: Conditions under which ensemble based systems may be more beneficial than their single classifier counterparts are reviewed, algorithms for generating individual components of the ensemble systems, and various procedures through which the individual classifiers can be combined are reviewed.
3.1K
On-line Boosting and Vision
Helmut Grabner,Horst Bischof +1 more
- 17 Jun 2006
TL;DR: This paper proposes a novel on-line AdaBoost feature selection method and demonstrates the multifariousness of the method on such diverse tasks as learning complex background models, visual tracking and object detection.
1.1K
Ensemble learning for data stream analysis
TL;DR: This paper surveys research on ensembles for data stream classification as well as regression tasks and discusses advanced learning concepts such as imbalanced data streams, novelty detection, active and semi-supervised learning, complex data representations and structured outputs.
1K
•Journal Article
Covariate Shift Adaptation by Importance Weighted Cross Validation
TL;DR: This paper proposes a new method called importance weighted cross validation (IWCV), for which its unbiasedness even under the covariate shift is proved, and the IWCV procedure is the only one that can be applied for unbiased classification under covariates.
References
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Yoav Freund,Robert E. Schapire +1 more
- 01 Aug 1997
TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
•Book
Self-Organizing Maps
Teuvo Kohonen
- 01 Jan 1995
TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
13.1K
Adaptive mixtures of local experts
TL;DR: A new supervised learning procedure for systems composed of many separate networks, each of which learns to handle a subset of the complete set of training cases, which is demonstrated to be able to be solved by a very simple expert network.
5.2K
Instance-Based Learning Algorithms
TL;DR: This paper describes how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy and extends the nearest neighbor algorithm, which has large storage requirements.
Mining high-speed data streams
Pedro Domingos,Geoff Hulten +1 more
- 01 Aug 2000
TL;DR: This paper describes and evaluates VFDT, an anytime system that builds decision trees using constant memory and constant time per example, and applies it to mining the continuous stream of Web access data from the whole University of Washington main campus.
Related Papers (5)
W. Nick Street,Yong Seog Kim +1 more
- 26 Aug 2001
Geoff Hulten,Laurie Spencer,Pedro Domingos +2 more
- 26 Aug 2001