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Probabilistic inductive logic programming
 In ALT
, 2004
"... Abstract. Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of diffe ..."
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Cited by 70 (9 self)
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Abstract. Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of different formalisms and learning techniques have been developed. A unifying characterization of the underlying learning settings, however, is missing so far. In this chapter, we start from inductive logic programming and sketch how the inductive logic programming formalisms, settings and techniques can be extended to the statistical case. More precisely, we outline three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces, and show how they can be adapted to cover stateoftheart statistical relational learning approaches. 1
Distributionbased aggregation for relational learning with identifier attributes
 Machine Learning
, 2004
"... Feature construction through aggregation plays an essential role in modeling relational domains with onetomany relationships between tables. Onetomany relationships lead to bags (multisets) of related entities, from which predictive information must be captured. This paper focuses on aggregation ..."
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Cited by 42 (10 self)
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Feature construction through aggregation plays an essential role in modeling relational domains with onetomany relationships between tables. Onetomany relationships lead to bags (multisets) of related entities, from which predictive information must be captured. This paper focuses on aggregation from categorical attributes that can take many values (e.g., object identifiers). We present a novel aggregation method as part of a relational learning system ACORA, that combines the use of vector distance and metadata about the classconditional distributions of attribute values. We provide a theoretical foundation for this approach deriving a “relational fixedeffect ” model within a Bayesian framework, and discuss the implications of identifier aggregation on the expressive power of the induced model. One advantage of using identifier attributes is the circumvention of limitations caused either by missing/unobserved object properties or by independence assumptions. Finally, we show empirically that the novel aggregators can generalize in the presence of identifier (and other highdimensional) attributes, and also explore the limitations of the applicability of the methods. 1
nFOIL: Integrating Naïve Bayes and FOIL
, 2005
"... We present the system nFOIL. It tightly integrates the naïve Bayes learning scheme with the inductive logic programming rulelearner FOIL. In contrast to previous combinations, which have employed naïve Bayes only for postprocessing the rule sets, nFOIL employs the naïve Bayes criterion to directly ..."
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Cited by 28 (5 self)
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We present the system nFOIL. It tightly integrates the naïve Bayes learning scheme with the inductive logic programming rulelearner FOIL. In contrast to previous combinations, which have employed naïve Bayes only for postprocessing the rule sets, nFOIL employs the naïve Bayes criterion to directly guide its search. Experimental evidence shows that nFOIL performs better than both its base line algorithm FOIL or the postprocessing approach, and is at the same time competitive with more sophisticated approaches.
Spatial Associative Classification: Propositional vs. Structural approach
 JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
, 2006
"... Spatial associative classification takes advantage of employing association rules for spatial classification purposes. In this work, we investigate spatial associative classification in multirelational data mining setting to deal with spatial objects having different properties, which are modeled ..."
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Cited by 18 (10 self)
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Spatial associative classification takes advantage of employing association rules for spatial classification purposes. In this work, we investigate spatial associative classification in multirelational data mining setting to deal with spatial objects having different properties, which are modeled by as many data tables (relations) as the number of spatial object types (layers). Spatial classification is based on two alternative approaches: a propositional approach and a structural approach. The propositional approach uses spatial association rules to construct an attributevalue representation (propositionalisation) of spatial data and performs spatial classification according to wellknown propositional classification methods. Since the attributevalue representation should capture relational properties of spatial data, multirelational association rules are used in propositionalisation step. The structural approach resorts to an extension of naïve Bayes classifiers to multirelational data where the classification is driven by multirelational association rules modelling regularities in spatial data. In both cases the spatial associative classification is performed at different levels of granularity and takes advantage from domain knowledge expressed in form of hierarchies and rules. Experiments on realworld georeferenced census data analysis show the advantage of the structural approach over the propositional one.
Bridging the gap between distance and generalisation: Symbolic learning in metric spaces
, 2008
"... Distancebased and generalisationbased methods are two families of artificial intelligence techniques that have been successfully used over a wide range of realworld problems. In the first case, general algorithms can be applied to any data representation by just changing the distance. The metric ..."
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Cited by 10 (4 self)
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Distancebased and generalisationbased methods are two families of artificial intelligence techniques that have been successfully used over a wide range of realworld problems. In the first case, general algorithms can be applied to any data representation by just changing the distance. The metric space sets the search and learning space, which is generally instanceoriented. In the second case, models can be obtained for a given pattern language, which can be comprehensible. The generalityordered space sets the search and learning space, which is generally modeloriented. However, the concepts of distance and generalisation clash in many different ways, especially when knowledge representation is complex (e.g. structured data). This work establishes a framework where these two fields can be integrated in a consistent way. We introduce the concept of distancebased generalisation, which connects all the generalised examples in such a way that all of them are reachable inside the generalisation by using straight paths in the metric space. This makes the metric space and the generalityordered space coherent (or even dual). Additionally, we also introduce a definition of minimal distancebased generalisation that can be seen as the first formulation of the Minimum Description Length (MDL)/Minimum Message Length (MML) principle in terms of a distance function. We instantiate and develop the framework for the most common data representations and distances, where we show that consistent instances can be found for numerical data, nominal data, sets, lists, tuples, graphs, firstorder atoms and clauses. As a result, general learning methods that integrate the best from distancebased and generalisationbased methods can be defined and adapted to any specific problem by appropriately choosing the distance, the pattern language and the generalisation operator.
Social Network Classification Incorporating Link Type Values
"... Abstract—Classification of nodes in a social network and its applications to security informatics have been extensively studied in the past. However, previous work generally does not consider the types of links (e.g., whether a person is friend or a close friend) that connect social networks members ..."
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Cited by 7 (0 self)
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Abstract—Classification of nodes in a social network and its applications to security informatics have been extensively studied in the past. However, previous work generally does not consider the types of links (e.g., whether a person is friend or a close friend) that connect social networks members for classification purposes. Here, we propose modified Naive Bayes Classification schemes to make use of the link type information in classification tasks. Basically, we suggest two new Bayesian classification methods that extend a traditional relational Naive Bayes Classifier, namely, the Link Type relational Bayes Classifier and the Weighted Link Type Bayes Classifier. We then show the efficacy of our proposed techniques by conducting experiments on data obtained from the Internet Movie Database. I.
Z.: Structure learning of probabilistic relational models from incomplete relational data. In: ECML
, 2007
"... Abstract. Existing relational learning approaches usually work on complete relational data, but realworld data are often incomplete. This paper proposes the MGDA approach to learn structures of probabilistic relational model (PRM) from incomplete relational data. The missing values are filled in ..."
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Abstract. Existing relational learning approaches usually work on complete relational data, but realworld data are often incomplete. This paper proposes the MGDA approach to learn structures of probabilistic relational model (PRM) from incomplete relational data. The missing values are filled in randomly at first, and a maximum likelihood tree (MLT) is generated from the complete data sample. Then, Gibbs sampling is combined with MLT to modify the data and regulate MLT iteratively for obtaining a wellcompleted data set. Finally, probabilistic structure is learned through dependency analysis from the completed data set. Experiments show that the MGDA approach can learn good structures from incomplete relational data. 1
AlShawakfa” A Comparison Study between Data Mining Tools over some Classification
 Methods” (IJACSA) International Journal of Advanced Computer Science and Applications, Special Issue on Artificial Intelligence
"... Abstract Nowadays, huge amount of data and information are available for everyone, Data can now be stored in many different kinds of databases and information repositories, besides being available on the Internet or in printed form. With such amount of data, there is a need for powerful techniques ..."
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Cited by 3 (0 self)
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Abstract Nowadays, huge amount of data and information are available for everyone, Data can now be stored in many different kinds of databases and information repositories, besides being available on the Internet or in printed form. With such amount of data, there is a need for powerful techniques for better interpretation of these data that exceeds the human's ability for comprehension and making decision in a better way. In order to reveal the best tools for dealing with the classification task that helps in decision making, this paper has conducted a comparative study between a number of some of the free available data mining and knowledge discovery tools and software packages. Results have showed that the performance of the tools for the classification task is affected by the kind of dataset used and by the way the classification algorithms were implemented within the toolkits. For the applicability issue, the WEKA toolkit has achieved the highest applicability followed by Orange, Tanagra, and KNIME respectively. Finally; WEKA toolkit has achieved the highest improvement in classification performance; when moving from the percentage split test mode to the Cross Validation test mode, followed by Orange, KNIME and finally Tanagra respectively.
Transductive Learning from Relational Data
 In: P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition, LNAI 4571
, 2007
"... Abstract. Transduction is an inference mechanism “from particular to particular”. Its application to classification tasks implies the use of both labeled (training) data and unlabeled (working) data to build a classifier whose main goal is that of classifying (only) unlabeled data as accurately as p ..."
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Abstract. Transduction is an inference mechanism “from particular to particular”. Its application to classification tasks implies the use of both labeled (training) data and unlabeled (working) data to build a classifier whose main goal is that of classifying (only) unlabeled data as accurately as possible. Unlike the classical inductive setting, no general rule valid for all possible instances is generated. Transductive learning is most suited for those applications where the examples for which a prediction is needed are already known when training the classifier. Several approaches have been proposed in the literature on building transductive classifiers from data stored in a single table of a relational database. Nonetheless, no attention has been paid to the application of the transduction principle in a (multi)relational setting, where data are stored in multiple tables of a relational database. In this paper we propose a new transductive classifier, named TRANSC, which is based on a probabilistic approach to making transductive inferences from relational data. This new method works in a transductive setting and employs a principled probabilistic classification in multirelational data mining to face the challenges posed by some spatial data mining problems. Probabilistic inference allows us to compute the class probability and return, in addition to result of transductive classification, the confidence in the classification. The predictive accuracy of TRANSC has been compared to that of its inductive counterpart in an empirical study involving both a benchmark relational dataset and two spatial datasets. The results obtained are generally in favor of TRANSC, although improvements are small by a narrow margin. 1
Simple Decision Forests for MultiRelational Classification
"... An important task in multirelational data mining is linkbased classification which takes advantage of attributes of links and linked entities, to predict the class label. The relational naive Bayes classifier exploits independence assumptions to achieve scalability. We introduce a weaker independe ..."
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An important task in multirelational data mining is linkbased classification which takes advantage of attributes of links and linked entities, to predict the class label. The relational naive Bayes classifier exploits independence assumptions to achieve scalability. We introduce a weaker independence assumption to the e↵ect that information from di↵erent data tables is independent given the class label. The independence assumption entails a closedform formula for combining probabilistic predictions based on decision trees learned on di↵erent database tables. Logistic regression learns di↵erent weights for information from di↵erent tables and prunes irrelevant tables. In experiments, learning was very fast with competitive accuracy.