Decision trees for mining data streams

By João Gama, Ricardo Fernandes, Ricardo Rocha. 2006

160 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tyler L. Hayes
2. Christopher Kanan
3. Gianluca Bontempi
4. Andrea Dal Pozzolo
5. Nathan D. Cahill
6. Vincenzo Lomonaco
7. S. Kotsiantis
8. Leszek Rutkowski

In Management Science and Operations Research

1. S. Kotsiantis
2. Gianluca Bontempi
3. Andrea Dal Pozzolo
4. Tyler L. Hayes
5. Christopher Kanan

Citations in the corpus

• Memory Efficient Experience Replay for Streaming Learning (2018, background): “Many are based on Hoeffding Decision Trees [6], [18], [25], [36],”
• Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis (2019, background): “Hoeffding Decision Trees [4, 16, 21, 27, 28] incrementally grow decision trees over time under the Hoeffding bound theoretical guarantees.”
• The CART decision tree for mining data streams (2014, methodology): “As the split measure function in the C4.5 algorithm, the ratio of the information gain and the split information is used.”
• Adaptive Machine Learning for Credit Card Fraud Detection (2015, methodology): “CVFDT [168] uses a sliding window for concept drift adaptation, VFDTc [169] extends VFDT for continuous data and CD using a Naive Bayes classifiers in the leaves.”
• Decision trees: a recent overview (2011, methodology): “To further improve VFDT, Gama et al. (2003) extended the VFDT system in two directions: the ability to deal with continuous data and the use of more powerful classification techniques at tree leaves.”