Sciweavers

Share
PAKDD
2010
ACM

Fast Perceptron Decision Tree Learning from Evolving Data Streams

9 years 11 months ago
Fast Perceptron Decision Tree Learning from Evolving Data Streams
Abstract. Mining of data streams must balance three evaluation dimensions: accuracy, time and memory. Excellent accuracy on data streams has been obtained with Naive Bayes Hoeffding Trees—Hoeffding Trees with naive Bayes models at the leaf nodes—albeit with increased runtime compared to standard Hoeffding Trees. In this paper, we show that runtime can be reduced by replacing naive Bayes with perceptron classifiers, while maintaining highly competitive accuracy. We also show that accuracy can be increased even further by combining majority vote, naive Bayes, and perceptrons. We evaluate four perceptron-based learning strategies and compare them against appropriate baselines: simple perceptrons, Perceptron Hoeffding Trees, hybrid Naive Bayes Perceptron Trees, and bagged versions thereof. We implement a perceptron that uses the sigmoid activation function instead of the threshold activation function and optimizes the squared error, with one perceptron per class value. We test our...
Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer
Added 20 Jul 2010
Updated 20 Jul 2010
Type Conference
Year 2010
Where PAKDD
Authors Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer, Eibe Frank
Comments (0)
books