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An adaptive regression tree for non-stationary data streams

Gholipour, A ; Sharif University of Technology | 2013

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  1. Type of Document: Article
  2. DOI: 10.1145/2480362.2480519
  3. Publisher: 2013
  4. Abstract:
  5. Data streams are endless flow of data produced in high speed, large size and usually non-stationary environments. The main property of these streams is the occurrence of concept drifts. Using decision trees is shown to be a powerful approach for accurate and fast learning of data streams. In this paper, we present an incremental regression tree that can predict the target variable of newly incoming instances. The tree is updated in the case of occurring concept drifts either by altering its structure or updating its embedded models. Experimental results show the effectiveness of our algorithm in speed and accuracy aspects in comparison to the best state-of-the-art methods
  6. Keywords:
  7. Concept drift ; Data streams ; Regression tree ; Concept drifts ; Data stream ; Embedded models ; Incremental regressions ; Non-stationary environment ; Nonstationary data ; Regression trees ; State-of-the-art methods ; Data communication systems ; Decision trees ; Forestry ; Algorithms ; Communication ; Data Bases ; Regression Analysis
  8. Source: Proceedings of the ACM Symposium on Applied Computing ; March , 2013 , Pages 815-816 ; 9781450316569 (ISBN)
  9. URL: http://dl.acm.org/citation.cfm?doid=2480362.2480519