Random rules from data streams
| Authors | |
|---|---|
| Year of publication | 2013 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 28th Annual ACM Symposium on Applied Computing, SAC '13 |
| MU Faculty or unit | |
| Citation | |
| Web | http://doi.acm.org/10.1145/2480362.2480518 |
| Doi | http://dx.doi.org/10.1145/2480362.2480518 |
| Field | Informatics |
| Keywords | Data Streams; Classification; Rule Learning; Random Rules |
| Description | 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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