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Transductive Learning for Spatial Data Classification

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Advances in Machine Learning I

Part of the book series: Studies in Computational Intelligence ((SCI,volume 262))

Abstract

Learning classifiers of spatial data presents several issues, such as the heterogeneity of spatial objects, the implicit definition of spatial relationships among objects, the spatial autocorrelation and the abundance of unlabelled data which potentially convey a large amount of information. The first three issues are due to the inherent structure of spatial units of analysis, which can be easily accommodated if a (multi-)relational data mining approach is considered. The fourth issue demands for the adoption of a transductive setting, which aims to make predictions for a given set of unlabelled data. Transduction is also motivated by the contiguity of the concept of positive autocorrelation, which typically affect spatial phenomena, with the smoothness assumption which characterize the transductive setting. In this work, we investigate a relational approach to spatial classification in a transductive setting. Computational solutions to the main difficulties met in this approach are presented. In particular, a relational upgrade of the naïve Bayes classifier is proposed as discriminative model, an iterative algorithm is designed for the transductive classification of unlabelled data, and a distance measure between relational descriptions of spatial objects is defined in order to determine the k-nearest neighbors of each example in the dataset. Computational solutions have been tested on two real-world spatial datasets. The transformation of spatial data into a multi-relational representation and experimental results are reported and commented.

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Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Bari, Via Orabona 4, I-70126, Bari, Italy

    Michelangelo Ceci, Annalisa Appice & Donato Malerba

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  1. Michelangelo Ceci
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  2. Annalisa Appice
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  3. Donato Malerba
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Editors and Affiliations

  1. Institute of Computer Science, Polish Academy of Sciences, ul.Ordona 21, 01-237, Warsaw, Poland

    Jacek Koronacki  & Sławomir T. Wierzchoń  & 

  2. Woodward Hall 430C University of North Carolina, 9201 University City Blvd., N.C. 28223, Charlotte, USA

    Zbigniew W. Raś

  3. Systems Research Institute, Polish Academy of Sciences, ul.Newelska 6, 01-447, Warsaw, 01-447

    Janusz Kacprzyk

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Ceci, M., Appice, A., Malerba, D. (2010). Transductive Learning for Spatial Data Classification. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds) Advances in Machine Learning I. Studies in Computational Intelligence, vol 262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05177-7_9

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  • DOI: https://doi.org/10.1007/978-3-642-05177-7_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05176-0

  • Online ISBN: 978-3-642-05177-7

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