Learning relational probability trees

Neville, Jennifer; Jensen, David; Friedland, Lisa; Hay, Michael

doi:10.1145/956804.956830

Cited by 83 publications

(149 citation statements)

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“…). Examples of this approach include probabilistic relational models [13] and relational probability trees [16]. Blockeel and Bruynooghe [5] discussed the resulting -often undesirable -bias on these learners, and proposed the idea of combining aggregation and selection.…”

Section: Related Workmentioning

confidence: 99%

Complex Aggregates over Clusters of Elements

Vens

Gassen

Dhaene

et al. 2015

Inductive Logic Programming

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Abstract. Complex aggregates have been proposed as a way to bridge the gap between approaches that handle sets by imposing conditions on specific elements, and approaches that handle them by imposing conditions on aggregated values. A complex aggregate summarises a subset of the elements in a set, where this subset is defined by conditions on the attribute values. In this paper, we present a new type of complex aggregate, where this subset is defined to be a cluster of the set. This is useful if subsets that are relevant for the task at hand are difficult to describe in terms of attribute conditions. This work is motivated from the analysis of flow cytometry data, where the sets are cells, and the subsets are cell populations. We describe two approaches to aggregate over clusters on an abstract level, and validate one of them empirically, motivating future research in this direction.

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Section: Related Workmentioning

confidence: 99%

Complex Aggregates over Clusters of Elements

Vens

Gassen

Dhaene

et al. 2015

Inductive Logic Programming

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“…For an elaborate treatment of this estimation techniques, please refer to the papers of Neville et al [10,11].…”

Section: Relational Bayesian Classifiers (Rbcs)mentioning

confidence: 99%

“…RPTs extend standard probability estimation trees (also called decision trees) to a relational setting, in which data instances are heterogeneous and interdependent [10]. The algorithm first transforms the relational data to multisets of attributes (Figure 1).…”

Section: Relational Probability Trees (Rpts)mentioning

confidence: 99%

“…We show that SRLs-we use Relational Probability Trees (RPTs) and Relational Bayesian Classifiers (RBCs) [10,11]-are able to exploit the rich and complex heterogeneous relational structure of Semantic Web data (Section 5).…”

Section: Introductionmentioning

confidence: 99%

“…These methods have been shown to be very powerful for SRL, as they model not only the intrinsic attributes of objects, but also the extrinsic relations to other objects [2,10,11].…”

Section: Background-a Brief Introduction To Srlmentioning

confidence: 99%

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Adding Data Mining Support to SPARQL Via Statistical Relational Learning Methods

Kiefer

Bernstein

Locher

Lecture Notes in Computer Science

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Abstract. Exploiting the complex structure of relational data enables to build better models by taking into account the additional information provided by the links between objects. We extend this idea to the Semantic Web by introducing our novel SPARQL-ML approach to perform data mining for Semantic Web data. Our approach is based on traditional SPARQL and statistical relational learning methods, such as Relational Probability Trees and Relational Bayesian Classifiers.We analyze our approach thoroughly conducting three sets of experiments on synthetic as well as real-world data sets. Our analytical results show that our approach can be used for any Semantic Web data set to perform instance-based learning and classification. A comparison to kernel methods used in Support Vector Machines shows that our approach is superior in terms of classification accuracy.

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Using spatiotemporal relational random forests to improve our understanding of severe weather processes

McGovern

Gagne

Troutman

et al. 2011

Statistical Analysis

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Abstract:Major severe weather events can cause a significant loss of life and property. We seek to revolutionize our understanding of and our ability to predict such events through the mining of severe weather data. Because weather is inherently a spatiotemporal phenomenon, mining such data requires a model capable of representing and reasoning about complex spatiotemporal dynamics, including temporally and spatially varying attributes and relationships. We introduce an augmented version of the Spatiotemporal Relational Random Forest, which is a random forest that learns with spatiotemporally varying relational data. Our algorithm maintains the strength and performance of random forests but extends their applicability, including the estimation of variable importance, to complex spatiotemporal relational domains. We apply the augmented Spatiotemporal Relational Random Forest to three severe weather data sets. These are: predicting atmospheric turbulence across the continental United States, examining the formation of tornadoes near strong frontal boundaries, and understanding the spatial evolution of drought across the southern plains of the United States. The results on such a wide variety of real-world domains demonstrate the extensive applicability of the Spatiotemporal Relational Random Forest. Our long-term goal is to significantly improve the ability to predict and warn about severe weather events. We expect that the tools and techniques we develop will be applicable to a wide range of complex spatiotemporal phenomena. 

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Learning relational probability trees

Cited by 83 publications

References 0 publications

Complex Aggregates over Clusters of Elements

Complex Aggregates over Clusters of Elements

Adding Data Mining Support to SPARQL Via Statistical Relational Learning Methods

Using spatiotemporal relational random forests to improve our understanding of severe weather processes

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