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Temporal abstraction and inductive logic programming for arrhythmia recogniton from electrocardiograms

ARTIFICIAL INTELLIGENCE IN MEDICINE 2003年第3期28卷 231-263页

作者： Carrault, G Cordier, MO Quiniou, R Wang, F IRISA INRIA F-35042 Rennes France Univ Rennes 1 IRISA F-35042 Rennes France LTSI F-35042 Rennes France

This paper proposes a novel approach to cardiac arrhythmia recognition from electrocardiograms (ECGs). ECGs record the electrical activity of the heart and are used to diagnose many heart disorders. The numerical ECG is first temporally abstracted into series of time-stamped events. Temporal abstraction makes use of artificial neural networks to extract interesting waves and their features from the input signals. A temporal reasoner called a chronicle recogniser processes such series in order to discover temporal patterns called chronicles which can be related to cardiac arrhythmias. Generally, it is difficult to elicit an accurate set of chronicles from a doctor. Thus, we propose to learn automatically from symbolic ECG examples the chronicles discriminating the arrhythmias belonging to some specific subset. Since temporal relationships are of major importance, inductive logic programming (ILP) is the tool of choice as it enables first-order relational learning. The approach has been evaluated on real ECGs taken from the MIT-BIH database. The performance of the different modules as well as the efficiency of the whole system is presented. The results are rather good and demonstrate that integrating numerical techniques for low level perception and symbolic techniques for high level classification is very valuable. (C) 2003 Elsevier B.V. All rights reserved.

关键词： ECG medical data analysis cardiac arrhythmia classification temporal abstraction artificial neural network inductive logic programming chronicle recognition

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Lifting symmetry breaking constraints with inductive logic programming

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MACHINE LEARNING 2022年第4期111卷 1303-1326页

作者： Tarzariol, Alice Gebser, Martin Schekotihin, Konstantin Univ Klagenfurt Klagenfurt Austria Univ Graz Univ Technol Graz Austria

Efficient omission of symmetric solution candidates is essential for combinatorial problem-solving. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced problem encodings might be problematic since the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances. As a result, a time-consuming recomputation of SBCs must be done before every invocation of a solver. To overcome these limitations, we introduce a new model-oriented approach for Answer Set programming that lifts the SBCs of small problem instances into a set of interpretable first-order constraints using the inductive logic programming paradigm. Experiments demonstrate the ability of our framework to learn general constraints from instance-specific SBCs for a collection of combinatorial problems. The obtained results indicate that our approach significantly outperforms a state-of-the-art instance-specific method as well as the direct application of a solver.

关键词： Answer set programming inductive logic programming Symmetry breaking constraints

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A new relational Tri-training system with adaptive data editing for inductive logic programming

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KNOWLEDGE-BASED SYSTEMS 2012年 35卷 173-185页

作者： Li, Yanjuan Guo, Maozu Harbin Inst Technol Sch Comp Sci & Technol Harbin 150006 Peoples R China NE Forestry Univ Sch Informat & Comp Engn Harbin Peoples R China

Relational Tri-training (k-Tri-training for short), as a relational semi-supervised learning system, can effectively exploit unlabeled examples to improve the generalization ability. However, the R-Tri-training may also suffer from the common problem in traditional semi-supervised learning, i.e., the performance is usually not stable for the unlabeled examples often be wrongly labeled and accumulated during the iterative learning process. In this paper, a new Relational Tri-training system named ADE-R-Tri-training (R-Tri-training with Adaptive Data Editing) is proposed. Not only does it employ a specific data editing technique to identify and correct the examples possibly mislabeled throughout the co-labeling iterations, but it also takes an adaptive strategy to decide whether to trigger the editing operation according to different cases. The adaptive strategy consists of five pre-conditional theorems, all of which ensure the iterative reduction of classification error under PAC (Probably Approximately Correct) learning theory. Experiments on well-known benchmarks show that ADE-R-Tri-training can more effectively enhance the performance of the hypothesis learned than R-Tri-training. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Machine learning inductive logic programming Tri-training Relational Tri-training Data editing Adaptive strategy PAC learning

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A phenotypic genetic algorithm for inductive logic programming

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第3期36卷 6935-6944页

作者： Chien, Ya-Wen Chang Chen, Yen-Liang Natl Cent Univ Dept Informat Management Jhongli 32001 Taoyuan County Taiwan

inductive logic programming (ILP) is one of the most popular approaches in machine learning. ILP can be used to automate the construction of first-order definite clause theories from examples and background knowledge. Although ILP has been successfully applied in various domains, it has the following weaknesses: (1) weak capabilities in numerical data processing. (2) zero noise tolerance, and (3) unsatisfactory learning time with a large number of arguments in the relation. This paper proposes a phenotypic genetic algorithm (PGA) to overcome these weaknesses. To strengthen the numerical data processing capabilities, a multiple level encoding structure is used that can represent three different types of relationships between two numerical data. To tolerate noise, PGA's goal of finding perfect rules is changed to finding top-k rules, which allows noise in the induction process. Finally, to shorten learning time, we incorporate the semantic roles constraint into PGA, reducing search space and preventing the discovery of infeasible rules. Stock trading data from Yahool Finance Online is used in our experiments. The results indicate that the PGA algorithm can find interesting trading rules from real data. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： inductive logic programming Phenotypic genetic algorithm Multiple level structure Semantic roles Numerical data

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A learning-based ontology alignment approach using inductive logic programming

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 125卷 412-424页

作者： Karimi, Hamed Kamandi, Ali Univ Tehran Coll Engn Sch Engn Sci Tehran Iran

Ontologies are key concepts in the semantic web and have an impressive role which comprise the biggest and the most prominent part of the infrastructure in this realm of web research. By fast growth of the semantic web and also, the variety of its applications, ontology mapping (ontology alignment) has been transformed into a crucial issue in the realm of computer science. Several approaches are introduced for ontology alignment during these last years, but developing more accurate and efficient algorithms and finding new effective techniques and algorithms for this problem is an interesting research area since real-world applications with respect to their more complicated concepts need more efficient algorithms. In this paper, we illustrated a new ontology mapping method based on learning using inductive logic programming (ILP), and show how the ILP can be used to solve the ontology mapping problem. As a matter of fact, in this approach, an ontology which is described in OWL format is interpreted to first-order logic. Then, with the use of learning based on inductive logic, the existing hidden rules and relationships between concepts are discovered and presented. Since the inductive logic has high flexibility in solving problems such as discovering relationships between concepts and links, it also can be performed effectively in solving the ontology alignment problem. Our experimental results show that this technique yield to more accurate results comparing to other matching algorithms and systems, achieving an F-measure of 95.6% and 91% on two well-known reference datasets the Anatomy and the Library, respectively. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Ontology alignment inductive logic programming Ontology matching Semantic web Ontology mapping learning

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Context-sensitive refinements for stochastic optimisation algorithms in inductive logic programming

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ARTIFICIAL INTELLIGENCE REVIEW 2011年第1期35卷 19-36页

作者： Buryan, Petr Kubalik, Jiri Czech Tech Univ Dept Cybernet CR-16635 Prague Czech Republic

We describe a new approach to the application of stochastic search in inductive logic programming (ILP). Unlike traditional approaches we do not focus directly on evolving logical concepts but our refinement-based approach uses the stochastic optimization process to iteratively adapt the initial working concept. Utilization of context-sensitive concept refinements (adaptations) helps the search operations to produce mostly syntactically correct concepts. It also enables using available background knowledge both for efficiently restricting the search space and for directing the search. Thereby, the search is more flexible, less problem-specific and the framework can be easily used with any stochastic search algorithm within ILP domain. Experimental results on several data sets verify the usefulness of this approach.

关键词： Evolutionary algorithms First order logic inductive logic programming Relational learning Stochastic search

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Efficient Program Synthesis Using Constraint Satisfaction in inductive logic programming

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JOURNAL OF MACHINE LEARNING RESEARCH 2013年第12期14卷 3649-3682页

作者： Ahlgren, John Yuen, Shiu yin City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

We present NrSample, a framework for program synthesis in inductive logic programming. NrSample uses propositional logic constraints to exclude undesirable candidates from the search. This is achieved by representing constraints as propositional formulae and solving the associated constraint satisfaction problem. We present a variety of such constraints: pruning, input-output, functional (arithmetic), and variable splitting. NrSample is also capable of detecting search space exhaustion, leading to further speedups in clause induction and optimality. We benchmark NrSample against enumeration search (Aleph's default) and Progol's A* search in the context of program synthesis. The results show that, on large program synthesis problems, NrSample induces between 1 and 1358 times faster than enumeration (236 times faster on average), always with similar or better accuracy. Compared to Progol A*, NrSample is 18 times faster on average with similar or better accuracy except for two problems: one in which Progol A* substantially sacrificed accuracy to induce faster, and one in which Progol A* was a clear winner. Functional constraints provide a speedup of up to 53 times (21 times on average) with similar or better accuracy. We also benchmark using a few concept learning (non-program synthesis) problems. The results indicate that without strong constraints, the overhead of solving constraints is not compensated for.

关键词： inductive logic programming program synthesis theory induction constraint satisfaction Boolean satisfiability problem

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Recognition of hand-printed characters based on structural description and inductive logic programming

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PATTERN RECOGNITION LETTERS 2003年第16期24卷 3187-3196页

作者： Amin, A Univ New S Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia

Character recognition systems can contribute tremendously to the advancement of the automation process and can improve the interaction between man and machine in many applications, including office automation, cheque verification and a large variety of banking, business and data entry applications. The main theme of this paper is the automatic recognition of hand-printed Arabic characters using machine learning. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. The advantages of machine learning are that it can generalize over the large degree of variation between writing styles and recognition rules can be constructed by example. The system was tested on a sample of handwritten characters from several individuals whose writing ranged from acceptable to poor in quality and the average correct recognitions rate obtained using cross-validation was 86.65%. (C) 2003 Elsevier B.V. All rights reserved.

关键词： pattern recognition Arabic characters hand-printed characters parallel thinning feature extraction structural classification inductive logic programming

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Improving scalability of inductive logic programming via pruning and best-effort optimisation

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 87卷 291-303页

作者： Kazmi, Mishal Schuller, Peter Saygin, Yucel Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey Marmara Univ Fac Engn Istanbul Turkey

inductive logic programming (ILP) combines rule-based and statistical artificial intelligence methods, by learning a hypothesis comprising.a set of rules given background knowledge and constraints for the search space. We focus on extending the XHAIL algorithm for ILP which is based on Answer Set programming and we evaluate our extensions using the Natural Language Processing application of sentence chunking. With respect to processing natural language, ILP can cater for the constant change in how we use language on a daily basis. At the same time, ILP does not require huge amounts of training examples such as other statistical methods and produces interpretable results, that means a set of rules, which can be analysed and tweaked if necessary. As contributions we extend XHAIL with (i) a pruning mechanism within the hypothesis generalisation algorithm which enables learning from larger datasets, (ii) a better usage of modern solver technology using recently developed optimisation methods, and (iii) a time budget that permits the usage of suboptimal results. We evaluate these improvements on the task of sentence chunking using three datasets from a recent SemEval competition. Results show that our improvements allow for learning on bigger datasets with results that are of similar quality to state-of-the-art Systems on the same task. Moreover, we compare the hypotheses obtained on datasets to gain insights on the structure of each dataset. (c) 2017 Elsevier Ltd. All rights reserved.

关键词： Answer Set programming inductive logic programming Natural Language Processing Chunking

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Editorial: inductive logic programming is coming of age

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MACHINE LEARNING 2001年第3期44卷 207-209页

作者： Flach, P Dzeroski, S Univ Bristol Bristol BS8 1UB Avon England Jozef Stefan Inst Ljubljana 1000 Slovenia

Issue Title: Special Issue: inductive logic programming

关键词： journals inductive logic programming Slovenia Machine Learning Artificial Intelligence

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