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63
Separateandconquer rule learning
 Artificial Intelligence Review
, 1999
"... This paper is a survey of inductive rule learning algorithms that use a separateandconquer strategy. This strategy can be traced back to the AQ learning system and still enjoys popularity as can be seen from its frequent use in inductive logic programming systems. We will put this wide variety of ..."
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Cited by 142 (29 self)
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This paper is a survey of inductive rule learning algorithms that use a separateandconquer strategy. This strategy can be traced back to the AQ learning system and still enjoys popularity as can be seen from its frequent use in inductive logic programming systems. We will put this wide variety of algorithms into a single framework and analyze them along three different dimensions, namely their search, language and overfitting avoidance biases.
Statistical relational learning for link prediction
 In Proceedings of the Workshop on Learning Statistical Models from Relational Data at IJCAI2003
, 2003
"... Link prediction is a complex, inherently relational, task. Be it in the domain of scientific citations, social networks or hypertext links, the underlying data are extremely noisy and the characteristics useful for prediction are not readily available in a “flat ” file format, but rather involve com ..."
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Cited by 68 (5 self)
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Link prediction is a complex, inherently relational, task. Be it in the domain of scientific citations, social networks or hypertext links, the underlying data are extremely noisy and the characteristics useful for prediction are not readily available in a “flat ” file format, but rather involve complex relationships among objects. In this paper, we propose the application of our methodology for Statistical Relational Learning to building link prediction models. We propose an integrated approach to building regression models from data stored in relational databases in which potential predictors are generated by structured search of the space of queries to the database, and then tested for inclusion in a logistic regression. We present experimental results for the task of predicting citations made in scientific literature using relational data taken from CiteSeer. This data includes the citation graph, authorship and publication venues of papers, as well as their word content. 1
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 56 (8 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
Pruning Algorithms for Rule Learning
, 1997
"... Prepruning and Postpruning are two standard techniques for handling noise in decision tree learning. Prepruning deals with noise during learning, while postpruning addresses this problem after an overfitting theory has been learned. We first review several adaptations of pre and postpruning te ..."
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Cited by 43 (15 self)
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Prepruning and Postpruning are two standard techniques for handling noise in decision tree learning. Prepruning deals with noise during learning, while postpruning addresses this problem after an overfitting theory has been learned. We first review several adaptations of pre and postpruning techniques for separateandconquer rule learning algorithms and discuss some fundamental problems. The primary goal of this paper is to show how to solve these problems with two new algorithms that combine and integrate pre and postpruning.
Statistical Relational Learning for Document Mining
, 2003
"... A major obstacle to fully integrated deployment of statistical learners is the assumption that data sits in a single table, even though most realworld databases have complex relational structures. In this paper, we introduce an integrated approach to building regression models from data stored ..."
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Cited by 39 (5 self)
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A major obstacle to fully integrated deployment of statistical learners is the assumption that data sits in a single table, even though most realworld databases have complex relational structures. In this paper, we introduce an integrated approach to building regression models from data stored in relational databases. Potential features are generated by structured search of the space of queries to the database, and then tested for inclusion in a logistic regression. We present experimental results for the task of predicting where scientific papers will be published based on relational data taken from CiteSeer. This data includes word counts in the document, frequently cited authors or papers, cocitations, publication venues of cited papers, word cooccurrences, and word counts in cited or citing documents. Our approach results in classification accuracies superior to those achieved when using classical "flat" features. Our classification task also serves as a "where to publish?" conference/journal recommendation task.
Learning FirstOrder Definitions of Functions
 Journal of Artificial Intelligence Research
, 1996
"... Firstorder learning involves finding a clauseform definition of a relation from examples of the relation and relevant background information. In this paper, a particular firstorder learning system is modified to customize it for finding definitions of functional relations. This restriction leads ..."
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Cited by 37 (1 self)
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Firstorder learning involves finding a clauseform definition of a relation from examples of the relation and relevant background information. In this paper, a particular firstorder learning system is modified to customize it for finding definitions of functional relations. This restriction leads to faster learning times and, in some cases, to definitions that have higher predictive accuracy. Other firstorder learning systems might benefit from similar specialization. 1. Introduction Empirical learning is the subfield of AI that develops algorithms for constructing theories from data. Most classification research in this area has used the attributevalue formalism, in which data are represented as vectors of values of a fixed set of attributes and are labelled with one of a small number of discrete classes. A learning system then develops a mapping from attribute values to classes that can be used to classify unseen data. Despite the welldocumented successes of algorithms develope...
Computational Intelligence Methods for RuleBased Data Understanding
 PROCEEDINGS OF THE IEEE
, 2004
"... ... This paper is focused on the extraction and use of logical rules for data understanding. All aspects of rule generation, optimization, and application are described, including the problem of finding good symbolic descriptors for continuous data, tradeoffs between accuracy and simplicity at the r ..."
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Cited by 28 (3 self)
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... This paper is focused on the extraction and use of logical rules for data understanding. All aspects of rule generation, optimization, and application are described, including the problem of finding good symbolic descriptors for continuous data, tradeoffs between accuracy and simplicity at the ruleextraction stage, and tradeoffs between rejection and error level at the rule optimization stage. Stability of rulebased description, calculation of probabilities from rules, and other related issues are also discussed. Major approaches to extraction of logical rules based on neural networks, decision trees, machine learning, and statistical methods are introduced. Optimization and application issues for sets of logical rules are described. Applications of such methods to benchmark and reallife problems are reported and illustrated with simple logical rules for many datasets. Challenges and new directions for research are outlined.
Clusterbased Concept Invention for Statistical Relational Learning
 Proceedings of the 10th SIGKDD
, 2004
"... We use clustering to derive new relations which augment database schema used in automatic generation of predictive features in statistical relational learning. Entities derived from clusters increase the expressivity of feature spaces by creating new firstclass concepts which contribute to the crea ..."
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Cited by 26 (5 self)
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We use clustering to derive new relations which augment database schema used in automatic generation of predictive features in statistical relational learning. Entities derived from clusters increase the expressivity of feature spaces by creating new firstclass concepts which contribute to the creation of new features. For example, in CiteSeer, papers can be clustered based on words or citations giving "topics", and authors can be clustered based on documents they coauthor giving "communities". Such clusterderived concepts become part of more complex feature expressions. Out of the large number of generated features, those which improve predictive accuracy are kept in the model, as decided by statistical feature selection criteria. We present results demonstrating improved accuracy on two tasks, venue prediction and link prediction, using CiteSeer data.
Towards Structural Logistic Regression: Combining Relational and Statistical Learning
, 2002
"... Inductive logic programming (ILP) techniques are useful for analyzing data in multitable relational databases. Learned rules can potentially discover relationships that are not obvious in ``flattened'' data. Statistical learners, on the other hand, are generally not constructed to search ..."
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Cited by 23 (5 self)
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Inductive logic programming (ILP) techniques are useful for analyzing data in multitable relational databases. Learned rules can potentially discover relationships that are not obvious in ``flattened'' data. Statistical learners, on the other hand, are generally not constructed to search relational data, they expect to be presented with a single table containing a set of feature candidates. However, statistical learners often yield more accurate models than the logical forms of ILP, and can better handle certain types of data, such as counts. We propose a new approach which integrates structure navigation from ILP with regression modeling. Our approach propositionalizes the firstorder rules at each step of ILP's relational structure search, generating features for potential inclusion in a regression model. Ideally, feature generation by ILP and feature selection by stepwise regression should be integrated into a single loop. Preliminary results for scientific literature classification are presented using a relational form of the data extracted by ResearchIndex (formerly CiteSeer). We use FOIL and logistic regression as our ILP and statistical components (decoupled at this stage). Word counts and citationbased features learned with FOIL are modeled together by logistic regression. The combination often significantly improves performance when high precision classification is desired.
The Independent Choice Logic and Beyond
"... Abstract. The Independent Choice Logic began in the early 90’s as a way to combine logic programming and probability into a coherent framework. The idea of the Independent Choice Logic is straightforward: there is a set of independent choices with a probability distribution over each choice, and a l ..."
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Cited by 18 (5 self)
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Abstract. The Independent Choice Logic began in the early 90’s as a way to combine logic programming and probability into a coherent framework. The idea of the Independent Choice Logic is straightforward: there is a set of independent choices with a probability distribution over each choice, and a logic program that gives the consequences of the choices. There is a measure over possible worlds that is defined by the probabilities of the independent choices, and what is true in each possible world is given by choices made in that world and the logic program. ICL is interesting because it is a simple, natural and expressive representation of rich probabilistic models. This paper gives an overview of the work done over the last decade and half, and points towards the considerable work ahead, particularly in the areas of lifted inference and the problems of existence and identity. 1