作者：
Yamamoto, AHokkaido Univ
Fac Technol & Meme Media Lab Kita Ku Sapporo Hokkaido 0608628 Japan
For given logical formulae B and E such that B K E, hypothesis finding means the generation of a formula H such that Bboolean ANDH satisfies E. Hypothesis finding constitutes a basic technique for fields of inference,...
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For given logical formulae B and E such that B K E, hypothesis finding means the generation of a formula H such that Bboolean ANDH satisfies E. Hypothesis finding constitutes a basic technique for fields of inference, like inductive inference and knowledge discovery. In order to put various hypothesis finding methods proposed previously on one general ground, we use upward refinement and residue hypotheses. We show that their combination is a complete method for solving any hypothesis finding problem in clausal logic. We extend the relative subsumption relation, and show that some hypothesis finding methods previously presented can be regarded as finding hypotheses which subsume examples relative to a given background theory. Noting that the weakening rule may make hypothesis finding difficult to solve, we propose restricting this rule either to the inverse of resolution or to that of subsumption. We also note that this work is related to relevant logic. (C) 2002 Elsevier Science B.V. All rights reserved.
inductive logic programming (ILP) is a form of machine learning that induces rules from data using the language and syntax of logicprogramming. A rule construction algorithm forms rules that summarize data sets. Thes...
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ISBN:
(纸本)0780362624
inductive logic programming (ILP) is a form of machine learning that induces rules from data using the language and syntax of logicprogramming. A rule construction algorithm forms rules that summarize data sets. These rules can be used in a large spectrum of data mining activities. In ILP, the rules are constructed with a target predicate as the consequent, or head, of the rule, and with high-ranking literals forming the antecedent, or body, of the rule. The predicate rankings are obtained by applying predicate ranking algorithms to a domain (background) knowledge base. In this work, we present three new predicate ranking algorithms for the inductive logic programming system, INDED (pronounced "indeed"). The algorithms use a grouping technique employing basic set theoretic operations to generate the rankings. We also present results of applying the ranking algorithms to several problem domains, some of which are universal like the classical genealogy problem, and others, not so common. In particular, diagnosis is the main thread of many of our experiments. Here, although our experimentation relates to medical diagnosis in diabetes and Lyme disease, many of the same techniques and methodologies can be applied to other forms of diagnosis including system failure, sensor detection, and trouble-shooting.
In this paper, we propose an inductive logic programming learning method which aims at automatically extracting special Noun-Verb (N-V) pairs from a corpus in order to build up semantic lexicons based on Pustejovsky&#...
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In this paper, we propose an inductive logic programming learning method which aims at automatically extracting special Noun-Verb (N-V) pairs from a corpus in order to build up semantic lexicons based on Pustejovsky's Generative Lexicon (GL) principles (Pustejovsky, 1995). In one of the components of this lexical model, called the qualia structure, words are described in terms of semantic roles. For example, the telic role indicates the purpose or function of an item (cut for knife), the agentive role its creation mode (build for house), etc. The qualia structure of a noun is mainly made up of verbal associations, encoding relational information. The inductive logic programming learning method that we have developed enables us to automatically extract from a corpus N-V pairs whose elements are linked by one of the semantic relations defined in the qualia structure in GL, and to distinguish them, in terms of surrounding categorial context from N-V pairs also present in sentences of the corpus but not relevant. This method has been theoretically and empirically validated, on a technical corpus. The N-V pairs that have been extracted will further be used in information retrieval applications for index expansion.
The study of protein structure has been driven largely by the careful inspection of experimental data by human experts. However, the rapid determination of protein structures from structural-genomics projects will mak...
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The study of protein structure has been driven largely by the careful inspection of experimental data by human experts. However, the rapid determination of protein structures from structural-genomics projects will make it increasingly difficult to analyse (and determine the principles responsible for) the distribution of proteins in fold space by inspection alone. Here, we demonstrate a machine-learning strategy that automatically determines the structural principles describing 45 folds. The rules learnt were shown to be both statistically significant and meaningful to protein experts. With the increasing emphasis on high-throughput experimental initiatives, machine-learning and other automated methods of analysis will become increasingly important for many biological problems. (C) 2003 Elsevier Ltd. All rights reserved.
The interest of introducing fuzzy predicates when learning rules is twofold. When dealing with numerical data, it enables us to avoid arbitrary discretization. Moreover, it enlarges the expressive power of what is lea...
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ISBN:
(纸本)3540200851
The interest of introducing fuzzy predicates when learning rules is twofold. When dealing with numerical data, it enables us to avoid arbitrary discretization. Moreover, it enlarges the expressive power of what is learned by considering different types of fuzzy rules, which may describe gradual behaviors of related attributes or uncertainty pervading conclusions. This paper describes different types of first-order fuzzy rules and a method for learning each type. Finally, we discuss the interest of each type of rules on a benchmark example.
A first-order Bayesian network (FOBN) is an extension of first-order logic in order to cope with uncertainty problems. Therefore, learning an FOBN might be a good idea to build an effective classifier. However, becaus...
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ISBN:
(纸本)3540403000
A first-order Bayesian network (FOBN) is an extension of first-order logic in order to cope with uncertainty problems. Therefore, learning an FOBN might be a good idea to build an effective classifier. However, because of a complication of the FOBN, directly learning it from relational data is difficult. This paper proposes another way to learn FOBN classifiers. We adapt inductive logic programming (ILP) and a Bayesian network learner to construct the FOBN. To do this, we propose a feature extraction algorithm to generate the significant parts (features) of ILP rules, and use these features as a main structure of the induced the FOBN. Next, to learn the remaining parts of the FOBN structure and its conditional probability tables by a standard Bayesian network learner, we also propose an efficient propositionalisation algorithm for translating the original data into the single table format. In this work, we provide a preliminary evaluation on the mutagenesis problem, a standard dataset for relational learning problem. The results are compared with the state-of-the-art ILP learner, the PROGOL system.
The paper presents an algorithm based on inductive logic programming for inducing first order Horn clauses involving fuzzy predicates from a database. For this, a probabilistic processing of fuzzy function is used, in...
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ISBN:
(纸本)3540403833
The paper presents an algorithm based on inductive logic programming for inducing first order Horn clauses involving fuzzy predicates from a database. For this, a probabilistic processing of fuzzy function is used, in agreement with the handling of probabilities in first order logic. This technique is illustrated on an experimental application. The interest of learning fuzzy first order logic expressions is emphasized.
The paper describes a method for inducing first-order rules with fuzzy predicates from a database. First, the paper makes a distinction between fuzzy rules allowing for some tolerance with respect to the interpretativ...
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ISBN:
(纸本)3540404945
The paper describes a method for inducing first-order rules with fuzzy predicates from a database. First, the paper makes a distinction between fuzzy rules allowing for some tolerance with respect to the interpretative scope of the predicates, and fuzzy rules aiming at expressing a set of ordinary rules in a global way. Moreover the paper only considers the induction of Horn-like implicative-based fuzzy rules. Specific confidence degrees are associated with each kind of fuzzy rules in the inductive process. This technique is illustrated on an experimental application. The interest of learning various types of fuzzy first-order logic expressions is emphasized.
In this paper, we address the characterization task and we present a general framework for the characterization of a target set of objects by means of their own properties, but also the properties of objects linked to...
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ISBN:
(纸本)3540200851
In this paper, we address the characterization task and we present a general framework for the characterization of a target set of objects by means of their own properties, but also the properties of objects linked to them. According to the kinds of objects, various links can be considered. For instance, in the case of relational databases, associations are the straightforward links between pairs of tables. We propose Caracterix, a new algorithm for mining characterization rules and we show how it can be used on multi-relational and spatial databases.
We proposed a new temporal reasoning based approach to recognise arrhythmias in real time. Arrhythmias are depicted by chronicle models consisting of a set of events linked by temporal constraints restricting the rang...
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ISBN:
(纸本)078037925X
We proposed a new temporal reasoning based approach to recognise arrhythmias in real time. Arrhythmias are depicted by chronicle models consisting of a set of events linked by temporal constraints restricting the range of the relative delay between their occurrence time. The temporal reasoner, called a chronicle recognition system, achieves arrhythmia recognition by detecting instances of these chronicle models on the input ECG signal previously transformed into a series of symbolic events. Experimental results demonstrate that the approach is a good complement for the existing methods based on complex QRS classification.
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