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...
详细信息
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...
详细信息
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...
详细信息
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...
详细信息
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...
详细信息
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...
详细信息
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.
An attempt is made to determine how machine learning benefit from using a richer and more expressive representation. The approach taken is to compare the performance of a so-called upgrade of a propositional algorithm...
详细信息
An attempt is made to determine how machine learning benefit from using a richer and more expressive representation. The approach taken is to compare the performance of a so-called upgrade of a propositional algorithm and the performance of a propositional learning algorithm applied to so-called propositionalization. This comparison is performed in learning domains that are essentially relational.
Cross-validation is a useful and generally applicable technique often employed in machine learning, including decision tree induction. An important disadvantage of straightforward implementation of the technique is it...
详细信息
Cross-validation is a useful and generally applicable technique often employed in machine learning, including decision tree induction. An important disadvantage of straightforward implementation of the technique is its computational overhead. In this paper we show that, for decision trees, the computational overhead of cross-validation can be reduced significantly by integrating the cross-validation with the normal decision tree induction process. We discuss how existing decision tree algorithms can be adapted to this aim, and provide an analysis of the speedups these adaptations may yield. We identify a number of parameters that influence the obtainable speedups, and validate and refine our analysis with experiments on a variety of data sets with two different implementations. Besides cross-validation, we also briefly explore the usefulness of these techniques for bagging. We conclude with some guidelines concerning when these optimizations should be considered.
inductive logic programming (ILP) is concerned with the induction of logic programs from examples and background knowledge. In ILP, the shift of attention from program synthesis to knowledge discovery resulted in adva...
详细信息
inductive logic programming (ILP) is concerned with the induction of logic programs from examples and background knowledge. In ILP, the shift of attention from program synthesis to knowledge discovery resulted in advanced techniques that are practically applicable for discovering knowledge in relational databases. This paper gives a brief introduction to ILP, presents selected ILP techniques for relational knowledge discovery and reviews selected ILP applications.
暂无评论