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Collective Inference for Handling Autocorrelation in Network Regression

  • Corrado Loglisci
  • Annalisa Appice
  • Donato Malerba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)

Abstract

In predictive data mining tasks, we should account for autocorrelations of both the independent variables and the dependent variable, which we can observe in neighborhood of a target node and that same node. The prediction on a target node should be based on the value of the neighbours which might even be unavailable. To address this problem, the values of the neighbours should be inferred collectively. We present a novel computational solution to perform collective inferences in a network regression task. We define an iterative algorithm, in order to make regression inferences about predictions of multiple nodes simultaneously and feed back the more reliable predictions made by the previous models in the labeled network. Experiments investigate the effectiveness of the proposed algorithm in spatial networks.

Keywords

Target Node Neighboring Variable Network Regression Entrepreneurship Ecosystem Predictive Inference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Appice, A., Džeroski, S.: Stepwise induction of multi-target model trees. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 502–509. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Arthur, G.: A history of the concept of spatial autocorrelation: A geographer’s perspective. Geographical Analysis 40(3), 297–309 (2008)CrossRefGoogle Scholar
  3. 3.
    Ohashi, O., Torgo, L.: Wind speed forecasting using spatio-temporal indicators. In: ECAI 2012, vol. 242, pp. 975–980. IOS Press (2012)Google Scholar
  4. 4.
    Stojanova, D., Ceci, M., Appice, A., Dzeroski, S.: Network regression with predictive clustering trees. Data Min. Knowl. Discov. 25(2), 378–413 (2012)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Corrado Loglisci
    • 1
  • Annalisa Appice
    • 1
  • Donato Malerba
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di Bari Aldo MoroBariItaly

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