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21
Naive Bayesian Classification of Structured Data
, 2003
"... In this paper we present 1BC and 1BC2, two systems that perform naive Bayesian classification of structured individuals. The approach of 1BC is to project the individuals along firstorder features. These features are built from the individual using structural predicates referring to related objects ..."
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Cited by 21 (0 self)
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In this paper we present 1BC and 1BC2, two systems that perform naive Bayesian classification of structured individuals. The approach of 1BC is to project the individuals along firstorder features. These features are built from the individual using structural predicates referring to related objects (e.g. atoms within molecules), and properties applying to the individual or one or several of its related objects (e.g. a bond between two atoms). We describe an individual in terms of elementary features consisting of zero or more structural predicates and one property; these features are treated as conditionally independent in the spirit of the naive Bayes assumption. 1BC2 represents an alternative firstorder upgrade to the naive Bayesian classifier by considering probability distributions over structured objects (e.g., a molecule as a set of atoms), and estimating those distributions from the probabilities of its elements (which are assumed to be independent). We present a unifying view on both systems in which 1BC works in language space, and 1BC2 works in individual space. We also present a new, efficient recursive algorithm improving upon the original propositionalisation approach of 1BC. Both systems have been implemented in the context of the firstorder descriptive learner Tertius, and we investigate the differences between the two systems both in computational terms and on artificially generated data. Finally, we describe a range of experiments on ILP benchmark data sets demonstrating the viability of our approach.
MrSBC: a MultiRelational Naive Bayes Classifier
 Todorovski & H. Blockeel (Eds.), Knowledge Discovery in Databases PKDD 2003, Lecture Notes in Artificial Intelligence
, 2003
"... Abstract. In this paper we propose an extension of the naïve Bayes classification method to the multirelational setting. In this setting, training data are stored in several tables related by foreign key constraints and each example is represented by a set of related tuples rather than a single row ..."
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Cited by 13 (5 self)
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Abstract. In this paper we propose an extension of the naïve Bayes classification method to the multirelational setting. In this setting, training data are stored in several tables related by foreign key constraints and each example is represented by a set of related tuples rather than a single row as in the classical data mining setting. This work is characterized by three aspects. First, an integrated approach in the computation of the posterior probabilities for each class that make use of first order classification rules. Second, the applicability to both discrete and continuous attributes by means a supervised discretization. Third, the consideration of knowledge on the data model embedded in the database schema during the generation of classification rules. The proposed method has been implemented in the new system MrSBC, which is tightly integrated with a relational DBMS. Testing has been performed on two datasets and four benchmark tasks. Results on predictive accuracy and efficiency are in favour of MrSBC for the most complex tasks. 1
The Role of Feature Construction in Inductive Rule Learning
"... This paper proposes a unifying framework for inductive rule learning algorithms. We suggest that the problem of constructing an appropriate inductive hypothesis (set of rules) can be broken down in the following subtasks: rule construction, body construction, and feature construction. Each of the ..."
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Cited by 12 (0 self)
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This paper proposes a unifying framework for inductive rule learning algorithms. We suggest that the problem of constructing an appropriate inductive hypothesis (set of rules) can be broken down in the following subtasks: rule construction, body construction, and feature construction. Each of these subtasks may have its own declarative bias, search strategies, and heuristics. In particular, we argue that feature construction is a crucial notion in explaining the relations between attributevalue rule learning and inductive logic programming (ILP). We demonstrate this by a general method for transforming ILP problems to attributevalue form, which overcomes some of the traditional limitations of propositionalisation approaches.
Schemas and Models
 IN PROCEEDINGS OF THE SIGKDD2002 WORKSHOP ON MULTIRELATIONAL LEARNING
, 2002
"... We propose the SchemaModel Framework, which characterizes algorithms that learn probabilistic models from relational data as having two parts: a schema that identifies sets of related data items and groups them into relevant categories; and a model that allows probabilistic inference about those ..."
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Cited by 10 (1 self)
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We propose the SchemaModel Framework, which characterizes algorithms that learn probabilistic models from relational data as having two parts: a schema that identifies sets of related data items and groups them into relevant categories; and a model that allows probabilistic inference about those data items. The framework
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 7 (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.
Logical Characterisations Of Inductive Learning
, 2000
"... This chapter presents a logical analysis of induction. Contrary to common approaches to inductive logic that treat inductive validity as a realvalued generalisation of deductive validity, we argue that the only logical step in induction lies in hypothesis generation rather than evaluation. ..."
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Cited by 7 (2 self)
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This chapter presents a logical analysis of induction. Contrary to common approaches to inductive logic that treat inductive validity as a realvalued generalisation of deductive validity, we argue that the only logical step in induction lies in hypothesis generation rather than evaluation.
FirstOrder Bayesian Classification with 1BC
, 2000
"... . In this paper we present 1BC, a firstorder Bayesian Classifier. Our approach is to view individuals as structured objects, and to distinguish between structural predicates referring to parts of individuals (e.g. atoms within molecules), and properties applying to the individual or one or several ..."
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Cited by 4 (2 self)
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. In this paper we present 1BC, a firstorder Bayesian Classifier. Our approach is to view individuals as structured objects, and to distinguish between structural predicates referring to parts of individuals (e.g. atoms within molecules), and properties applying to the individual or one or several of its parts (e.g. a bond between two atoms). We describe an individual in terms of elementary features consisting of zero or more structural predicates and one property; these features are considered conditionally independent following the usual naive Bayes assumption. 1BC has been implemented in the context of the firstorder descriptive learner Tertius, and we describe several experiments demonstrating the viability of our approach. Keywords: inductive logic programming, naive Bayes, firstorder logic 1. Motivation and scope In this paper we present 1BC, a firstorder Bayesian Classifier. While the propositional Bayesian Classifier makes the naive Bayes assumption of statistical indepe...
Learning in Clausal Logic: A Perspective on Inductive Logic Programming
 Computational Logic: Logic Programming and Beyond, volume 2407 of Lecture Notes in Computer Science
, 2002
"... Abstract. Inductive logic programming is a form of machine learning from examples which employs the representation formalism of clausal logic. One of the earliest inductive logic programming systems was Ehud Shapiro’s Model Inference System [90], which could synthesise simple recursive programs like ..."
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Cited by 3 (0 self)
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Abstract. Inductive logic programming is a form of machine learning from examples which employs the representation formalism of clausal logic. One of the earliest inductive logic programming systems was Ehud Shapiro’s Model Inference System [90], which could synthesise simple recursive programs like append/3. Many of the techniques devised by Shapiro, such as topdown search of program clauses by refinement operators, the use of intensional background knowledge, and the capability of inducing recursive clauses, are still in use today. On the other hand, significant advances have been made regarding dealing with noisy data, efficient heuristic and stochastic search methods, the use of logical representations going beyond definite clauses, and restricting the search space by means of declarative bias. The latter is a general term denoting any form of restrictions on the syntactic form of possible hypotheses. These include the use of types, input/output mode declarations, and clause schemata. Recently, some researchers have started using alternatives to Prolog featuring strong typing and real functions, which alleviate the need for some of the above adhoc mechanisms. Others have gone beyond Prolog by investigating learning tasks in which the hypotheses are not definite clause programs, but for instance sets of indefinite clauses or denials, constraint logic programs, or clauses representing association rules. The chapter gives an accessible introduction to the above topics. In addition, it outlines the main current research directions which have been strongly influenced by recent developments in data mining and challenging reallife applications. 1
Automatic Induction of Abduction and Abstraction Theories from Observations
, 2005
"... Traditional Machine Learning approaches are based on single inference mechanisms. A step forward concerned the integration of multiple inference strategies within a firstorder logic learning framework, taking advantage of the benefits that each approach can bring. ..."
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Cited by 3 (3 self)
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Traditional Machine Learning approaches are based on single inference mechanisms. A step forward concerned the integration of multiple inference strategies within a firstorder logic learning framework, taking advantage of the benefits that each approach can bring.
VERBAL BEHAVIOR OF THE MORE AND THE LESS INFLUENTIAL MEETING PARTICIPANT ABSTRACT
"... We test the strength of the relationship between the way that people behave in a discussion and their level of influence on the basis of some empirical grounds. We use the data sources that were collected from the AMI corpus for the experiments in the areas of argumentation, dialogueact and influenc ..."
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Cited by 1 (0 self)
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We test the strength of the relationship between the way that people behave in a discussion and their level of influence on the basis of some empirical grounds. We use the data sources that were collected from the AMI corpus for the experiments in the areas of argumentation, dialogueact and influence research. Statistical dependencies and (cor)relations between the tags are mined for possible relationships. 1.