Random rules from data streams

Varování

Publikace nespadá pod Filozofickou fakultu, ale pod Fakultu informatiky. Oficiální stránka publikace je na webu muni.cz.
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EZILDA Almeida KOSINA Petr GAMA Joao

Rok publikování 2013
Druh Článek ve sborníku
Konference Proceedings of the 28th Annual ACM Symposium on Applied Computing, SAC '13
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www http://doi.acm.org/10.1145/2480362.2480518
Doi http://dx.doi.org/10.1145/2480362.2480518
Obor Informatika
Klíčová slova Data Streams; Classification; Rule Learning; Random Rules
Popis Existing works suggest that random inputs and random features produce good results in classification. In this paper we study the problem of generating random rule sets from data streams. One of the most interpretable and flexible models for data stream mining prediction tasks is the Very Fast Decision Rules learner (VFDR). In this work we extend the VFDR algorithm using random rules from data streams. The proposed algorithm generates several sets of rules. Each rule set is associated with a set of Natt attributes. The proposed algorithm maintains all properties required when learning from stationary data streams: online and any-time classification, processing each example once.
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