COBBIT—A control procedure for COBWEB in the presence of concept drift

Kilander, Fredrik; Jansson, Carl Gustaf

doi:10.1007/3-540-56602-3_140

Cited by 12 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The work presented here relates to incremental or on-line concept learning, which has recently received considerable attention among theoreticians (e.g., Angluin, 1988;Maass, 1991;Helmbold, Littlestone, & Long, 1992) as well as practitioners (e.g., Schlimmer & Granger, 1986;Langley, Gennari, & Iba, 1987;Kilander & Jansson, 1993;Kubat, 1989Kubat, , 1993Salganicoff, 1993a;Widmer & Kubat, 1993), The principal task is to learn a concept incrementally by processing labeled training examples one at a time. From another point of view, the problem may be seen as minimizing the total number of erroneous classifications in a feedback system: a stream of objects are classified, one by one, as positive or negative instances of a concept, and immediately afterwards the correct answer is received.…”

Section: Introductionmentioning

confidence: 99%

Learning in the presence of concept drift and hidden contexts

1996

View full text Add to dashboard Cite

On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as concept drift. We describe a family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear. The general approach underlying all these algorithms consists of (1) keeping only a window of currently trusted examples and hypotheses; (2) storing concept descriptions and reusing them when a previous context reappears ; and (3) controlling both of these functions by a heuristic that constantly monitors the system's behavior. The paper reports on experiments that test the systems' performance under various conditions such as different levels of noise and different extent and rate of concept drift.

show abstract

Section: Introductionmentioning

confidence: 99%

Learning in the presence of concept drift and hidden contexts

1996

View full text Add to dashboard Cite

show abstract

“…But none of these systems can actually discard old, harmful information, and none of them explicitly stores previous concept descriptions. However, recent work on modified versions of COBWEB (Kilander & Jansson, 1993) is very mush related to our approach and seems to yield very promising results.…”

mentioning

confidence: 99%

Effective learning in dynamic environments by explicit context tracking

Widmer

Kubát

1993

Machine Learning: ECML-93

127

View full text Add to dashboard Cite

Daily experience shows that in the real world, the meaning of many concepts heavily depends on some implicit context, and changes in that context can cause radical changes in the concepts. This paper introduces a method for incremental concept learning in dynamic environments where the target concepts may be context-dependent and may change drastically over time. The method has been implemented in a system called FLORA3. FLORA3 is very flexible in adapting to changes in the target concepts and tracking concept drift. Moreover, by explicitly storing old hypotheses and re-using them to bias learning in new contexts, it possesses the ability to utilize experience from previous learning. This greatly increases the system's effectiveness in environments where contexts can reoccur periodically. The paper describes the various algorithms that constitute the method and reports on several experiments that demonstrate the flexibility of FLORA3 in dynamic environments.

show abstract

“…Computational learning theory has also investigated the problem (Hembold & Long, 1994;Kuh et al, 1991). In unsupervised learning, the system COBBIT (Kilander & Jansson, 1993) warrants some mention.…”

Section: Previous Work Handling Concept Driftmentioning

confidence: 99%

Applying lazy learning algorithms to tackle concept drift in spam filtering

Fdez-Riverola

Iglesias

Díaz

et al. 2007

Expert Systems with Applications

103

View full text Add to dashboard Cite

A great amount of machine learning techniques have been applied to problems where data is collected over an extended period of time. However, the disadvantage with many real-world applications is that the distribution underlying the data is likely to change over time. In these situations, a problem that many global eager learners face is their inability to adapt to local concept drift. Concept drift in spam is particularly difficult as the spammers actively change the nature of their messages to elude spam filters. Algorithms that track concept drift must be able to identify a change in the target concept (spam or legitimate e-mails) without direct knowledge of the underlying shift in distribution. In this paper we show how a previously successful instance-based reasoning e-mail filtering model can be improved in order to better track concept drift in spam domain. Our proposal is based on the definition of two complementary techniques able to select both terms and e-mails representative of the current situation. The enhanced system is evaluated against other well-known successful lazy learning approaches in two scenarios, all within a cost-sensitive framework. The results obtained from the experiments carried out are very promising and back up the idea that instance-based reasoning systems can offer a number of advantages tackling concept drift in dynamic problems, as in the case of the anti-spam filtering domain.

show abstract

COBBIT—A control procedure for COBWEB in the presence of concept drift

Cited by 12 publications

References 13 publications

Learning in the presence of concept drift and hidden contexts

Learning in the presence of concept drift and hidden contexts

Effective learning in dynamic environments by explicit context tracking

Applying lazy learning algorithms to tackle concept drift in spam filtering

Contact Info

Product

Resources

About