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66
Clausal Discovery
 Machine Learning
, 1996
"... The clausal discovery engine Claudien is presented. Claudien is an inductive logic programming engine that fits in the knowledge discovery in databases and data mining paradigm as it discovers regularities that are valid in data. As such Claudien performs a novel induction task, which is called char ..."
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Cited by 184 (33 self)
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The clausal discovery engine Claudien is presented. Claudien is an inductive logic programming engine that fits in the knowledge discovery in databases and data mining paradigm as it discovers regularities that are valid in data. As such Claudien performs a novel induction task, which is called characteristic induction from closed observations, and which is related to existing formalizations of induction in logic. In characterising induction from closed observations, the regularities are represented by clausal theories, and the data using Herbrand interpretations. Claudien also employs a novel declarative bias mechanism to define the set of clauses that may appear in a hypothesis. Keywords : Inductive Logic Programming, Knowledge Discovery in Databases, Data Mining, Learning, Induction, Semantics for Induction, Logic of Induction, Parallel Learning. 1 Introduction Despite the fact that the areas of knowledge discovery in databases [Fayyad et al., 1995] and inductive logic programmin...
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 136 (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.
Topdown induction of clustering trees
 In 15th Int’l Conf. on Machine Learning
, 1998
"... An approach to clustering is presented that adapts the basic topdown induction of decision trees method towards clustering. To this aim, it employs the principles of instance based learning. The resulting methodology is implemented in the TIC (Top down Induction of Clustering trees) system for firs ..."
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Cited by 101 (22 self)
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An approach to clustering is presented that adapts the basic topdown induction of decision trees method towards clustering. To this aim, it employs the principles of instance based learning. The resulting methodology is implemented in the TIC (Top down Induction of Clustering trees) system for first order clustering. The TIC system employs the first order logical decision tree representation of the inductive logic programming system Tilde. Various experiments with TIC are presented, in both propositional and relational domains. 1
Improving the efficiency of inductive logic programming through the use of query packs
 JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
, 2002
"... Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or data mining. However, in order for ILP to become practically useful, the efficiency of ILP systems must improve substantially. To this end, the notion of a query pack is introduced: it structures sets ..."
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Cited by 56 (19 self)
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Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or data mining. However, in order for ILP to become practically useful, the efficiency of ILP systems must improve substantially. To this end, the notion of a query pack is introduced: it structures sets of similar queries. Furthermore, a mechanism is described for executing such query packs. A complexity analysis shows that considerable efficiency improvements can be achieved through the use of this query pack execution mechanism. This claim is supported by empirical results obtained by incorporating support for query pack execution in two existing learning systems.
Kernels and Distances for Structured Data
 Machine Learning
, 2004
"... This paper brings together two strands of machine learning of increasing importance: kernel methods and highly structured data. We propose a general method for constructing a kernel following the syntactic structure of the data, as defined by its type signature in a higherorder logic. Our main theo ..."
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Cited by 51 (3 self)
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This paper brings together two strands of machine learning of increasing importance: kernel methods and highly structured data. We propose a general method for constructing a kernel following the syntactic structure of the data, as defined by its type signature in a higherorder logic. Our main theoretical result is the positive definiteness of any kernel thus defined. We report encouraging experimental results on a range of realworld datasets. By converting our kernel to a distance pseudometric for 1nearest neighbour, we were able to improve the best accuracy from the literature on the Diterpene dataset by more than 10%.
ROC 'n' Rule Learning  Towards a Better Understanding of Covering Algorithms
 Machine Learning
, 2005
"... This paper provides an analysis of the behavior of separateandconquer or covering rule learning algorithms by visualizing their evaluation metrics and their dynamics in PNspace, a variant of ROCspace. Our results show that most commonly used search heuristics, including accuracy, weighted relativ ..."
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Cited by 48 (13 self)
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This paper provides an analysis of the behavior of separateandconquer or covering rule learning algorithms by visualizing their evaluation metrics and their dynamics in PNspace, a variant of ROCspace. Our results show that most commonly used search heuristics, including accuracy, weighted relative accuracy, entropy, and Gini index, are equivalent to one of two fundamental prototypes: precision, which tries to optimize the area under the ROC curve for unknown costs, and a costweighted difference between covered positive and negative examples, which tries to find the optimal point under known or assumed costs. We also show that a straightforward generalization of the mestimate trades off these two prototypes. Furthermore, our results show that stopping and filtering criteria like CN2's significance test focus on identifying significant deviations from random classification, which does not necessarily avoid overfitting. We also identify a problem with Foil's MDLbased encoding length restriction, which proves to be largely equivalent to a variable threshold on the recall of the rule. In general, we interpret these results as evidence that, contrary to common conception, prepruning heuristics are not very well understood and deserve more investigation.
Scaling up inductive logic programming by learning from interpretations. Data Mining and Knowledge Discovery
 Data Mining and Knowledge Discovery
, 1999
"... Abstract. When comparing inductive logic programming (ILP) and attributevalue learning techniques, there is a tradeoff between expressive power and efficiency. Inductive logic programming techniques are typically more expressive but also less efficient. Therefore, the data sets handled by current ..."
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Cited by 42 (14 self)
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Abstract. When comparing inductive logic programming (ILP) and attributevalue learning techniques, there is a tradeoff between expressive power and efficiency. Inductive logic programming techniques are typically more expressive but also less efficient. Therefore, the data sets handled by current inductive logic programming systems are small according to general standards within the data mining community. The main source of inefficiency lies in the assumption that several examples may be related to each other, so they cannot be handled independently. Within the learning from interpretations framework for inductive logic programming this assumption is unnecessary, which allows to scale up existing ILP algorithms. In this paper we explain this learning setting in the context of relational databases. We relate the setting to propositional data mining and to the classical ILP setting, and show that learning from interpretations corresponds to learning from multiple relations and thus extends the expressiveness of propositional learning, while maintaining its efficiency to a large extent (which is not the case in the classical ILP setting). As a case study, we present two alternative implementations of the ILP system Tilde (Topdown Induction of Logical DEcision trees): Tildeclassic, which loads all data in main memory, and TildeLDS, which loads the examples one by one. We experimentally compare the implementations, showing TildeLDS can handle large data sets (in the order of 100,000 examples or 100 MB) and indeed scales up linearly in the number of examples.
Maximum Entropy Modeling with Clausal Constraints
 In Proceedings of the 7th International Workshop on Inductive Logic Programming
, 1997
"... We present the learning system Maccent which addresses the novel task of stochastic MAximum ENTropy modeling with Clausal Constraints. Maximum Entropy method is a Bayesian method based on the principle that the target stochastic model should be as uniform as possible, subject to known constraints. ..."
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Cited by 37 (1 self)
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We present the learning system Maccent which addresses the novel task of stochastic MAximum ENTropy modeling with Clausal Constraints. Maximum Entropy method is a Bayesian method based on the principle that the target stochastic model should be as uniform as possible, subject to known constraints. Maccent incorporates clausal constraints that are based on the evaluation of Prolog clauses in examples represented as Prolog programs. We build on an existing maximumlikelihood approach to maximum entropy modeling, which we upgrade along two dimensions: (1) Maccent can handle larger search spaces, due to a partial ordering defined on the space of clausal constraints, and (2) uses a richer firstorder logic format. In comparison with other inductive logic programming systems, Maccent seems to be the first that explicitly constructs a conditional probability distribution p(CjI) based on an empirical distribution ~ p(CjI) (where p(CjI) (~p(CjI)) gives the induced (observed) probability of ...
Topdown induction of logical decision trees
 Artificial Intelligence
, 1998
"... Topdown induction of decision trees (TDIDT) is a very popular machine learning technique. Up till now, it has mainly been used for propositional learning, but seldomly for relational learning or inductive logic programming. The main contribution of this paper is the introduction of logical decision ..."
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Cited by 31 (1 self)
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Topdown induction of decision trees (TDIDT) is a very popular machine learning technique. Up till now, it has mainly been used for propositional learning, but seldomly for relational learning or inductive logic programming. The main contribution of this paper is the introduction of logical decision trees, which make it possible to use TDIDT in inductive logic programming. An implementation of this topdown induction of logical decision trees, the Tilde system, is presented and experimentally evaluated. 1
A Framework for Defining Distances Between FirstOrder Logic Objects
, 1998
"... this paper we develop a framework for distances between clauses and distances between models. The framework can be parametrised by a measure for the distance between atoms. It takes into account subterms common to distinct atoms of a set of atoms in the measurement of the distance between sets. More ..."
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Cited by 30 (3 self)
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this paper we develop a framework for distances between clauses and distances between models. The framework can be parametrised by a measure for the distance between atoms. It takes into account subterms common to distinct atoms of a set of atoms in the measurement of the distance between sets. Moreover, for a constant number of variables, the complexity of the distance computation is polynomially bounded by the size of the objects. Initial experiments show that the framework can be the basis of good clustering algorithms. The framework consists of three levels: At the first level one chooses a distance between atoms . The second level upgrades this distance to a distance between sets of atoms. We propose a framework that is a generalisation of three polynomial time computable similarity measures proposed by Eiter and Mannila, and an instance which is a real distance function, computable in polynomial time. We develop also a binary prototype function for sets of points. Prototype fun