Random rules from data streams

• Autoři: 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
• 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.

Související projekty

• Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity
• Zastoupení ČR v European Research Consortium for Informatics and Mathematics