Efficient decision tree construction on streaming data

By Ruoming Jin, Gagan Agrawal. 2003

184 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

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. João Gama
2. Geoffrey I. Webb
3. Saso Dzeroski
4. Elena Ikonomovska
5. Mohamed Medhat Gaber
6. Plamen Parvanov Angelov
7. Levente Kocsis
8. Róbert Pálovics

In Computer Networks and Communications

1. Plamen Parvanov Angelov
2. Josh Herbach
3. João Gama
4. Roberto J. Bayardo Jr.
5. Saso Dzeroski
6. Mohamed Medhat Gaber
7. Elena Ikonomovska
8. Geoffrey I. Webb

In Signal Processing

1. João Gama
2. Mohamed Medhat Gaber
3. Saso Dzeroski
4. Elena Ikonomovska
5. Plamen Parvanov Angelov
6. Geoffrey I. Webb
7. Leszek Rutkowski
8. Shonali Priyadarsini Krishnaswamy

Citations in the corpus

• Memory Efficient Experience Replay for Streaming Learning (2018, neutral): “Many are based on Hoeffding Decision Trees [6], [19], [26], [37], [38], [39], [40], [57], [64] or ensemble methods [8], [10], [28], [45], [47], [52], [55], [62], [71], [73].”
• PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce (2009, background): “Approximate algorithms for parallel learning of trees on streaming data have also been recently proposed [2, 20].”
• A Survey of Classification Methods in Data Streams (2007, methodology): “Techniques surveyed include the Ensemble-based Classification [30], Very Fast Decision Trees (VFDT) [9] with its extensions [22], [23], On-Demand Classification [3], On-Line Information Network (OLIN) [26], Lightweight Classification (LWClass) [14], Scalable Classification Algorithm by Learning…”
• Online Machine Learning in Big Data Streams (2018, methodology): “In [127], a method is described that partitions the range of a numerical attribute into intervals and uses statistical tests to”
• The CART decision tree for mining data streams (2014, methodology): “Following the idea of [18] we propose a new method to determine the best attribute to split the considered node.”
• Learning model trees from evolving data streams (2010, methodology): “2003), and the NIP-H and NIP-N algorithms (Jin and Agrawal 2003).”
• Evolving Fuzzy-Rule-Based Classifiers From Data Streams (2008, methodology): “Fuzzy-rule-based (FRB) systems have become one of the alternative frameworks for classifier design, together with the more established Bayesian classifiers [8]–[10], decision trees [11], [12], and more recently, neural network (NN)-based classifiers [13] and support vector machines [14].”
• Extremely Fast Decision Tree (2018, background): “There is a sizable literature that adapts HT in sometimes substantial ways [12, 19, 23] that do not, to the best of our knowledge, lead to the same fundamental change in learning premise as does HATT.”