Please use this identifier to cite or link to this item:
https://hdl.handle.net/10216/83804| Author(s): | Ezilda Almeida Petr Kosina João Gama |
| Title: | Random rules from data streams |
| Issue Date: | 2013 |
| Abstract: | 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. Copyright 2013 ACM. |
| Subject: | Ciência de computadores, Ciências da computação e da informação Computer science, Computer and information sciences |
| Scientific areas: | Ciências exactas e naturais::Ciências da computação e da informação Natural sciences::Computer and information sciences |
| URI: | https://hdl.handle.net/10216/83804 |
| Source: | Proceedings of the ACM Symposium on Applied Computing |
| Document Type: | Artigo em Livro de Atas de Conferência Internacional |
| Rights: | openAccess |
| License: | https://creativecommons.org/licenses/by-nc/4.0/ |
| Appears in Collections: | FEP - Artigo em Livro de Atas de Conferência Internacional |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 50582.pdf | Random rules from data streams | 400.49 kB | Adobe PDF |  View/Open |
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