Global and Local Spatial Autocorrelation in Predictive Clustering Trees

Stojanova, Daniela; Ceci, Michelangelo; Appice, Annalisa; Malerba, Donato; Džeroski, Sašo

doi:10.1007/978-3-642-24477-3_25

Cited by 18 publications

(17 citation statements)

References 18 publications

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“…Stojanova et al [13] propose a definition of spatial entropy based on global and local autocorrelation in a predictive cluster tree. The difference is that their approach is designed for geographical regression.…”

Section: Discussionmentioning

confidence: 99%

Learning spatial decision tree for geographical classification

Jiang

Shekhar

Mohan

et al. 2012

Proceedings of the 20th International Conference on Advances in Geographic Information Systems

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Given learning samples from a spatial raster dataset, the geographical classification problem aims to learn a decision tree classifier that minimizes classification errors as well as salt-n-pepper noise. The problem is important in many applications, such as land cover classification in remote sensing and lesion classification in medical diagnosis. However, the problem is challenging due to spatial autocorrelation. Existing decision tree learning algorithms, i.e. ID3, C4.5, CART, produce a lot of salt-n-pepper noise in classification results, due to their assumption that data items are drawn independently from identical distributions. In contrast, we propose a spatial decision tree learning algorithm, which incorporates spatial autocorrelation effect by a new spatial information gain (SIG) measure. The proposed approach is evaluated in a case study on a remote sensing dataset from Chanhassen, MN. Case study results show that the proposed approach outperforms the traditional approach in not only reducing salt-n-pepper noise but also improving classification accuracy.

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Section: Discussionmentioning

confidence: 99%

Learning spatial decision tree for geographical classification

Jiang

Shekhar

Mohan

et al. 2012

Proceedings of the 20th International Conference on Advances in Geographic Information Systems

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“…An initial implementation of the method for considering spatial autocorrelation when learning PCTs for regression was presented at a machine learning conference (Stojanova et al, 2011). In this paper, we present significant further developments and extensions in the following directions:…”

Section: Introductionmentioning

confidence: 99%

Dealing with spatial autocorrelation when learning predictive clustering trees

Stojanova

Ceci

Appice

et al. 2013

Ecological Informatics

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Spatial autocorrelation is the correlation among data values which is strictly due to the relative spatial proximity of the objects that the data refer to. Inappropriate treatment of data with spatial dependencies, where spatial autocorrelation is ignored, can obfuscate important insights. In this paper, we propose a data mining method that explicitly considers spatial autocorrelation in the values of the response (target) variable when learning predictive clustering models. The method is based on the concept of predictive clustering trees (PCTs), according to which hierarchies of clusters of similar data are identified and a predictive model is associated to each cluster. In particular, our approach is able to learn predictive models for both a continuous response (regression task) and a discrete response (classification task). We evaluate our approach on several real world problems of spatial regression and spatial classification. The consideration of the autocorrelation in the models improves predictions that are consistently clustered in space and that clusters try to preserve the spatial arrangement of the data, at the same time providing a multi-level insight into the spatial autocorrelation phenomenon. The evaluation of SCLUS in several ecological domains (e.g. predicting outcrossing rates within a conventional field due to the surrounding genetically modified fields, as well as predicting pollen dispersal rates from two lines of plants) confirms its capability of building spatial aware models which capture the spatial distribution of the target variable. In general, the maps obtained by using SCLUS do not require further post-smoothing of the results if we want to use them in practice.

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“…Over time, several researchers have worked in the realm of hierarchical classification. Stojanova et al [32] proposed a global-based method to consider self-correlation, i.e., the statistical relationships between the same variable at different but related instances. During training, a combination of features and self-correlation among cases is used.…”

Section: Hierarchical Classificationmentioning

confidence: 99%

Hierarchy-Based File Fragment Classification

Bhatt

Mishra

Kabir

et al. 2020

MAKE

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File fragment classification is an essential problem in digital forensics. Although several attempts had been made to solve this challenging problem, a general solution has not been found. In this work, we propose a hierarchical machine-learning-based approach with optimized support vector machines (SVM) as the base classifiers for file fragment classification. This approach consists of more general classifiers at the top level and more specialized fine-grain classifiers at the lower levels of the hierarchy. We also propose a primitive taxonomy for file types that can be used to perform hierarchical classification. We evaluate our model with a dataset of 14 file types, with 1000 fragments measuring 512 bytes from each file type derived from a subset of the publicly available Digital Corpora, the govdocs1 corpus. Our experiment shows comparable results to the present literature, with an average accuracy of 67.78% and an F1-measure of 65% using 10-fold cross-validation. We then improve on the hierarchy and find better results, with an increase in the F1-measure of 1%. Finally, we make our assessment and observations, then conclude the paper by discussing the scope of future research.

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Global and Local Spatial Autocorrelation in Predictive Clustering Trees

Cited by 18 publications

References 18 publications

Learning spatial decision tree for geographical classification

Learning spatial decision tree for geographical classification

Dealing with spatial autocorrelation when learning predictive clustering trees

Hierarchy-Based File Fragment Classification

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