Name
Abstract | ||
|---|---|---|
Data stream mining targets the learning of predictive models that evolve over time according to changes in arriving data. Throughout the years, several approaches have been tailored to create and continuously update predictive models from these streams, and from these, Hoeffding Trees became a popular choice for learning decision trees from data streams. In this paper, we aim at quantifying and expressing the importance of features in dynamic scenarios is of the utmost importance as they allow domain experts to back up, or invalidate, a predictive model. Therefore, we propose and assess a positional gain method tailored for for both individual and ensembles of Hoeffding Trees and how these behave in both synthetic and real-world scenarios.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3297280.3297551 | SAC |
Keywords | Field | DocType |
concept drift, data stream mining, feature ranking | Data mining,Decision tree,Data stream mining,Computer science,Feature ranking,Concept drift,Concept drifting,STREAMS | Conference |
ISBN | Citations | PageRank |
978-1-4503-5933-7 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jean Antonio Pereira Karax | 1 | 0 | 0.34 |
| Andreia Malucelli | 2 | 113 | 28.00 |
| Jean Paul Barddal | 3 | 140 | 16.77 |