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Regression Trees for Streaming Data with Local Performance Guarantees
Högskolan i Borås, Institutionen Handels- och IT-högskolan.
Högskolan i Borås, Institutionen Handels- och IT-högskolan.
Högskolan i Borås, Institutionen Handels- och IT-högskolan.
Högskolan i Borås, Institutionen Handels- och IT-högskolan.
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Online predictive modeling of streaming data is a key task for big data analytics. In this paper, a novel approach for efficient online learning of regression trees is proposed, which continuously updates, rather than retrains, the tree as more labeled data become available. A conformal predictor outputs prediction sets instead of point predictions; which for regression translates into prediction intervals. The key property of a conformal predictor is that it is always valid, i.e., the error rate, on novel data, is bounded by a preset significance level. Here, we suggest applying Mondrian conformal prediction on top of the resulting models, in order to obtain regression trees where not only the tree, but also each and every rule, corresponding to a path from the root node to a leaf, is valid. Using Mondrian conformal prediction, it becomes possible to analyze and explore the different rules separately, knowing that their accuracy, in the long run, will not be below the preset significance level. An empirical investigation, using 17 publicly available data sets, confirms that the resulting rules are independently valid, but also shows that the prediction intervals are smaller, on average, than when only the global model is required to be valid. All-in-all, the suggested method provides a data miner or a decision maker with highly informative predictive models of streaming data.

Place, publisher, year, edition, pages
IEEE , 2014.
Keywords [en]
Conformal Prediction, Streaming data, Regression trees, Interpretable models, Machine learning, Data mining
National Category
Computer Sciences Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-221541DOI: 10.1109/BigData.2014.7004263Local ID: 2320/14627ISBN: 978-1-4799-5666-1 (print)OAI: oai:DiVA.org:kth-221541DiVA, id: diva2:1175266
Conference
IEEE International Conference on Big Data, 27-30 October, 2014, Washington, DC, USA
Note

Sponsorship:

This work was supported by the Swedish Foundation for Strategic

Research through the project High-Performance Data Mining for Drug Effect

Detection (IIS11-0053), the Swedish Retail and Wholesale Development

Council through the project Innovative Business Intelligence Tools (2013:5)

and the Knowledge Foundation through the project Big Data Analytics by

Online Ensemble Learning (20120192).

QC 20180209

Available from: 2018-01-17 Created: 2018-01-17 Last updated: 2018-02-09Bibliographically approved

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CiteExportLink to record
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Citation style
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Language
  • de-DE
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