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On Applying Probabilistic logic programming to Breast Cancer Data 1

27th international conference on inductive logic programming (ilp)

作者： Corte-Real, Joana Dutra, Ines Rocha, Ricardo Univ Porto Fac Sci Rua Campo Alegre 1021-1055 P-4169007 Porto Portugal Univ Porto CRACS & INESC TEC Rua Campo Alegre 1021-1055 P-4169007 Porto Portugal

ISBN: (数字)9783319780900

ISBN: (纸本)9783319780900;9783319780894

Medical data is particularly interesting as a subject for relational data mining due to the complex interactions which exist between different entities. Furthermore, the ambiguity of medical imaging causes interpretation to be complex and error-prone, and thus particularly amenable to improvement through automated decision support. Probabilistic inductive logic programming (Pilp) is a particularly well-suited tool for this task, since it makes it possible to combine the relational nature of this field with the ambiguity inherent in human interpretation of medical imaging. this work presents a Pilp setting for breast cancer data, where several clinical and demographic variables were collected retrospectively, and new probabilistic variables and rules reflecting domain knowledge were introduced. A Pilp predictive model was built automatically from this data and experiments show that it can not only match the predictions of a team of experts in the area, but also consistently reduce the error rate of malignancy prediction, when compared to other non-relational techniques.

关键词： Medical imaging

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Relational Restricted Boltzmann Machines: A Probabilistic logic Learning Approach 1

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27th international conference on inductive logic programming (ilp)

作者： Kaur, Navdeep Kunapuli, Gautam Khot, Tushar Kersting, Kristian Cohen, William Natarajan, Sriraam Indiana Univ Bloomington IN 47405 USA Univ Texas Dallas Richardson TX 75083 USA Allen Inst Artificial Intelligence Seattle WA USA Tech Univ Darmstadt Darmstadt Germany Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (数字)9783319780900

ISBN: (纸本)9783319780900;9783319780894

We consider the problem of learning Boltzmann machine classifiers from relational data. Our goal is to extend the deep belief framework of RBMs to statistical relational models. this allows one to exploit the feature hierarchies and the non-linearity inherent in RBMs over the rich representations used in statistical relational learning (SRL). Specifically, we use lifted random walks to generate features for predicates that are then used to construct the observed features in the RBM in a manner similar to Markov logic Networks. We show empirically that this method of constructing an RBM is comparable or better than the state-of-the-art probabilistic relational learning algorithms on six relational domains.

关键词： Probabilistic logics

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A Genetic Algorithm-based ilp Incremental System 12

A Genetic Algorithm-based ILP Incremental System

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12th international Scientific and Technical conference on Computer Sciences and Information Technologies (CSIT)

作者： Al-Jamimi, Hamdi A. Ahmed, Moataz King Fahd Univ Petr & Minerals Informat & Comp Sci Dept Dhahran 31261 Saudi Arabia

ISBN: (纸本)9781538616390

inductive learning has been employed successfully in various domains, however the inductive logic programming (ilp) systems focused on non-incremental learning tasks where independent sets of data are provided incoherently. In this paper, we propose a new genetic algorithm-based ilp system, called GAilp, for incremental learning. GAilp is a covering algorithm which extracts hypotheses/rules from a collection of examples in a reliable way. It employs a genetic algorithm technique to discover various aspects of the potential combinations. GAilp induces every possible rule for the given combination and selects the most generic ones among them. It also eliminates rules which might become obsolete by the existence of more generic rules. Unlike other ilp systems, GAilp batches all given examples and background knowledge, then it groups the examples and prioritizes the induction process. this prioritization needs to be done to preserve dependency and to revise theory. the paper introduces GAilp's fundamentals mechanisms and demonstrates its algorithms with a running example.

关键词： inductive logic programming inductive learning learning by examples

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Relational data factorization

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MACHINE LEARNING 2017年第12期106卷 1867-1904页

作者： Paramonov, Sergey van Leeuwen, Matthijs De Raedt, Luc Katholieke Univ Leuven Machine Learning Dept Comp Sci Leuven Belgium Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands

Motivated by an analogy with matrix factorization, we introduce the problem of factorizing relational data. In matrix factorization, one is given a matrix and has to factorize it as a product of other matrices. In relational data factorization, the task is to factorize a given relation as a conjunctive query over other relations, i.e., as a combination of natural join operations. Given a conjunctive query and the input relation, the problem is to compute the extensions of the relations used in the query. thus, relational data factorization is a relational analog of matrix factorization;it is also a form of inverse querying as one has to compute the relations in the query from the result of the query. the result of relational data factorization is neither necessarily unique nor required to be a lossless decomposition of the original relation. therefore, constraints can be imposed on the desired factorization and a scoring function is used to determine its quality (often similarity to the original data). Relational data factorization is thus a constraint satisfaction and optimization problem. We show how answer set programming can be used for solving relational data factorization problems.

关键词： Answer set programming inductive logic programming Pattern mining Relational data Factorization Data mining Declarative modeling

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Proposal of application method of inductive logic programming to microarray data 17

Proposal of application method of Inductive Logic Programmin...

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8th international conference on Computational Systems-Biology and Bioinformatics (CSBio)

作者： Ide, Hiromu Umezawa, Masakazu Ohwada, Hayato Tokyo Univ Sci 2641 Yamazaki Noda Chiba 2788510 Japan

ISBN: (纸本)9781450353502

this paper describing a method of specifying common terms of genes from microarray data in 3 steps. First, we use random forest for extracting disease-related genes and it give each gene variable importance. the higher the variable importance, the more effective feature for classification. We extract genes whose variable importance more than 0 and set them positive samples and the rest set negative samples for ilp. Next, we annotate extracted genes by using Gene Ontology (GO) and use the term as predicate for ilp. Annotation is the process of assigning GO terms to gene products. Finally, we obtain rules about common terms in positive samples by using ilp. ilp is a subfield of machine learning which uses logic programming as a uniform representation technique for examples, background knowledge and hypotheses. ilp learns based on background knowledge. Background knowledge is represented in first-order logic. In the result, we extracted 1051 mRNA as positive samples for ilp from random forest and its F-measure score was 65.1%. We obtained about 4000 terms at each dataset and use them as predicates for ilp. We got eventually some rules about positive samples.

关键词： Bioinformatics Machine learning random forest Annotation ilp

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Relation Extraction from Texts with Symbolic Rules Induced by inductive logic programming 27

Relation Extraction from Texts with Symbolic Rules Induced b...

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27th IEEE international conference on Tools with Artificial Intelligence (ICTAI)

作者： Lima, Rinaldo Freitas, Fred Espinasse, Bernard Univ Fed Pernambuco Informat Ctr Recife PE Brazil Aix Marseille Univ LSIS Marseille France

ISBN: (纸本)9781509001637

Relation Extraction ( RE) is the task of detecting semantic relations between entities in text. Most of the state-of-the-art RE systems rely on statistical machine learning techniques which usually employ an attribute-value representation of features. Contrarily to this trend, we focus on an alternative approach to RE based on the automatic induction of symbolic extraction rules. We present OntoilpER, an RE system based on inductive logic programming which uses a domain ontology in its extraction process. Several experiments are discussed in this paper over the reACE 2004/2005 reference corpora. the results are encouraging and seem to demonstrate the effectiveness of the proposed solution.

关键词： Relation Extraction Ontology-based Information Extraction Ontology Population inductive logic programming

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An abductive-inductive algorithm for probabilistic inductive logic programming 26

An abductive-inductive algorithm for probabilistic inductive...

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26th international conference on inductive logic programming (Short Papers), ilp 2016

作者： Dragiev, Stanislav Russo, Alessandra Broda, Krysia Law, Mark Turliuc, Rares Department of Computing Imperial College London United Kingdom

the integration of abduction and induction has lead to a variety of non-monotonic ilp systems. XHAIL is one of these systems, in which abduction is used to compute hypotheses that subsume Kernel Sets. On the other hand, Peircebayes is a recently proposed logic-based probabilistic programming approach that combines abduction with parameter learning to learn distributions of most likely explanations. In this paper, we propose an approach for integrating probabilistic inference with ilp. the basic idea is to redefine the inductive task of XHAIL as a statistical abduction, and to use Peircebayes to learn probability distribution of hypotheses. An initial evaluation of the proposed algorithm is given using synthetic data.

关键词： Probability distributions

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9th international Workshop on inductive logic programming, ilp 1999

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9th international Workshop on inductive logic programming, ilp 1999

ISBN: (纸本)3540661093

the proceedings contain 27 papers. the special focus in this conference is on inductive logic programming. the topics include: Probabilistic relational models;inductive databases (abstract);some elements of machine learning (extended abstract);refinement operators can be (weakly) perfect;combining divide-and-conquer and separate-and-conquer for efficient and;effective rule induction;refining complete hypotheses in ilp;acquiring graphic design knowledge with nonmonotonic inductive learning;morphosyntactic tagging of Slovene using progol;experiments in predicting biodegradability;a first-order Bayesian classifier;building background knowledge into a refinement operator for inductive logic programming;a strong complete schema for inductive functional logic programming;application of different learning methods to Hungarian part-of-speech tagging;combining lapis and wordnet for the learning of LR parsers with optimal semantic constraints;learning word segmentation rules for tag prediction;approximate ilp rules by back propagation neural network;rule evaluation measures: a unifying view;improving part-of-speech disambiguation rules by adding linguistic knowledge;on sufficient conditions for learnability of logic programs from positive data;a bounded search space of clausal theories;discovering new knowledge from graph data using inductive logic programming;analogical prediction;generalizing refinement operators to learn prenex conjunctive normal forms;theory recovery;instance based function learning;some properties of inverse resolution in normal logic programs;an assessment of ilp-assisted models for toxicology and the PTE-3 experiment.

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inductive logic programming 1

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丛书名： Lecture Notes in Computer Science

1000年

ISBN: (数字)9783540690597

ISBN: (纸本)9783540647386

this book constitutes the refereed proceedings of the 8th international conference on inductive logic programming, ilp-98, held in Madison, Wisconsin, USA, in July 1998.;the 27 revised full papers presented together with the abstracts of three invited talks were carefully reviewed and selected for inclusion in the book. All relevant aspects of inductive logic programming are covered ranging from theory to implementations and applications.

关键词： Software Engineering/programming and Operating Systems Artificial Intelligence programming Techniques

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Learning logic program representation for delayed systems with limited training data 27

Learning logic program representation for delayed systems wi...

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Late Breaking Papers of the 27th international conference on inductive logic programming, LBP-ilp 2017

作者： Phua, Yin Jun Ribeiro, Tony Tourret, Sophie Inoue, Katsumi Tokyo Institute of Technology Japan National Institute of Informatics Japan Laboratoire des Sciences Du Numérique de Nantes France

Understanding the inuences between components of dynamical systems such as biological networks, cellular automata or social networks provides insights to their dynamics. Inuences of such dynamical sys- tems can be represented by logic programs with delays. logical methods that learn logic programs from observations have been developed, but their practical use is limited since they cannot handle noisy input and need a huge amount of data to give accurate results. In this pa- per, we present a method that learns to distinguish different dynamical systems with delays based on Recurrent Neural Network (RNN). this method relies on Long Short-Term Memory (LSTM) to extract and encode features from input sequences of time series data. We show that the produced high dimensional encoding can be used to distinguish different dynamical systems and reproduce their specific behaviors. © by the paper's authors.

关键词： Dynamical systems

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