Very Fast Decision Rules for Multi-class Problems

Varování

Publikace nespadá pod Ekonomicko-správní fakultu, ale pod Fakultu informatiky. Oficiální stránka publikace je na webu muni.cz.
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KOSINA Petr GAMA Joao

Rok publikování 2012
Druh Článek ve sborníku
Konference ACM 27th Symposium On Applied Computing
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
Obor Informatika
Klíčová slova Data Streams; Classification; Rule Learning
Popis Decision rules are one of the most interpretable and flexible models for data mining prediction tasks. Till now, few works presented on-line, any-time and one-pass algorithms for learning decision rules in the stream mining scenario. A quite recent algorithm, the Very Fast Decision Rules (VFDR), learns set of rules, where each rule discriminates one class from all the other. In this work we extend the VFDR algorithm by decomposing a multi-class problem into a set of two-class problems and inducing a set of discriminative rules for each binary problem. The proposed algorithm maintains all properties required when learning from stationary data streams: on-line and any-time classifiers, processing each example once. Moreover, it is able to learn ordered and unordered rule sets. The new approach is evaluated on various real and artificial datasets. The new algorithm improves the performance of the previous version and is competitive with the state-of-the-art decision tree learning method for data streams.
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