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Advantages of decision lists and implicit negatives in inductive logic programming

NEW GENERATION COMPUTING 1998年第3期16卷 263-281页

作者： Califf, ME Mooney, RJ Univ Texas Dept Comp Sci Austin TX 78712 USA

This paper demonstrates the capabilities of FOIDL, an inductive logic programming (ILP) system whose distinguishing characteristics are the ability to produce first-order decision lists, the use of an output completeness assumption as a substitute for negative examples, and the use of intensional background knowledge. The development of FOIDL was originally motivated by the problem of learning to generate the past tense of English verbs;however, this paper demonstrates its superior performance on two different sets of benchmark ILP problems. Tests on the finite element mesh design problem show that FOIDL's decision lists enable it to produce generally more accurate results than a range of methods previously applied to this problem. Tests with a selection of list-processing problems from Bratko's introductory Prolog text demonstrate that the combination of implicit negatives and intensionality allow FOIDL to learn correct programs from far fewer examples than FOIL.

关键词： inductive logic programming machine learning decision lists

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Knowledge base for finite-element mesh design learned by inductive logic programming

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AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING 1998年第2期12卷 95-106页

作者： Dolsak, B Bratko, I Jezernik, A Univ Maribor Fac Mech Engn SLO-2000 Maribor Slovenia Univ Ljubljana Fac Comp & Informat Sci Ljubljana 61000 Slovenia

This paper addresses an important application of machine learning (ML) in design. One of the major bottlenecks in the process of engineering analysis by using the finite-element method-a design of the finite-element mesh-was a subject of improvement. Defining an appropriate geometric mesh model that ensures low approximation errors and avoids unnecessary computational overhead is a very difficult and time-consuming task based mainly on the user's experience. A knowledge base for finite-element mesh design has been constructed using the ML techniques. Ten mesh models have been used as a source of training examples. The mesh dataset was probably the first real-world relational dataset and became one of the most widely used training set for experimenting with inductive logic programming (ILP) systems. After several experiments with different ML systems in the last few years, the ILP system CLAUDIEN was chosen to construct the rules for determining the appropriate mesh resolution values. The ILP has been found to be an effective approach to the problem of mesh design. An evaluation of the resulting knowledge base shows that the mesh design patterns are captured well by the induced rules and represent a solid basis for practical application. The aim of this paper is not only to present the real-life ML application to design, but also to describe and discuss a relation of the work being done to the topic of this special issue: the proposed "dimensions" of ML in design.

关键词： finite-element mesh design inductive logic programming knowledge base machine learning

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Prediction of ordinal classes using regression trees

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FUNDAMENTA INFORMATICAE 2001年第1-2期47卷 1-13页

作者： Kramer, S Pfahringer, B Widmer, G De Groeve, M Univ Freiburg Inst Comp Sci D-79110 Freiburg Germany Univ Waikato Dept Comp Sci Hamilton New Zealand Univ Vienna Dept Med Cybernet & AI A-1010 Vienna Austria Austrian Res Inst Artificial Intelligence A-1010 Vienna Austria Katholieke Univ Leuven Dept Comp Sci Louvain Belgium

This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes using classification and regression trees. We start with S-CART, a tree induction algorithm, and study various ways of transforming it into a learner for ordinal classification tasks. These algorithm variants are compared on a number of benchmark data sets to verify the relative strengths and weaknesses of the strategies and to study the trade-off between optimal categorical classification accuracy (hit rate) and minimum distance-based error. Preliminary results indicate that this is a promising avenue towards algorithms that combine aspects of classification and regression.

关键词： machine learning ordinal classes regression trees decision trees classification regression inductive logic programming

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Discovering knowledge from graph structured data by using refutably inductive inference of formal graph systems

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2001年第1期E84D卷 48-56页

作者： Miyahara, T Uchida, T Shoudai, T Kuboyama, T Takahashi, K Ueda, H Hiroshima City Univ Fac Informat Sci Hiroshima 7313194 Japan Kyushu Univ Dept Informat Kasuga Fukuoka 8168580 Japan Univ Tokyo Ctr Collaborat Res Tokyo 1530041 Japan

We present a new method for discovering knowledge from structured data which are represented Ly graphs in the framework of inductive logic programming. A graph, or network, is widely used for representing relations between various data and expressing a small and easily understandable hypothesis. The analyzing system directly manipulating graphs is useful for knowledge discovery. Our method uses Formal Graph System (FGS) as a knowledge representation language for graph structured data. FGS is a kind of logic programming system which directly deals with graphs just like first order terms. And our method employs a refutably inductive inference algorithm as a learning algorithm. A refutably inductive inference algorithm is a special type of inductive inference algorithm with refutability of hypothesis spaces. and is suitable for knowledge discovery. We give a sufficiently large hypothesis space, the set of weakly reducing FGS programs. And we show that this hypothesis space is refutably inferable from complete data. We have designed and implemented a prototype of a knowledge discovery system KD-FGS, which is based on our method and acquires knowledge directly from graph structured data. Finally we discuss the applicability of our method for graph structured data with experimental results on some graph theoretical notions.

关键词： knowledge discovery graph structured data inductive logic programming refutably inductive inference

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Prototyping structural description using an inductive learning program

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS 2000年第1期30卷 150-157页

作者： Amin, A Univ New S Wales Sch Comp Sci 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, check 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 correct average recognition rate obtained using cross-validation was 89.65%.

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

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Levelwise search and pruning strategies for first-order hypothesis spaces

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2000年第2-3期14卷 217-239页

作者： Weber, I Univ Stuttgart Inst Informat D-70565 Stuttgart Germany

The discovery of interesting patterns in relational databases is an important data mining task. This paper is concerned with the development of a search algorithm for first-order hypothesis spaces adopting an important pruning technique (termed subset pruning here) from association rule mining in a first-order setting. The basic search algorithm is extended by so-called requires and excludes constraints allowing to declare prior knowledge about the data, such as mutual exclusion or generalization relationships among attributes, so that it can be exploited for further structuring and restricting the search space. Furthermore, it is illustrated how to process taxonomies and numerical attributes in the search algorithm. Several task settings using different interestingness criteria and search modes with corresponding pruning criteria are described. Three settings serve as test beds for evaluation of the proposed approach. The experimental evaluation shows that the impact of subset pruning is significant, since it reduces the number of hypothesis evaluations in many cases by about 50%. The impact of generalization relationships is shown to be less effective in our experimental set-up.

关键词： data mining inductive logic programming levelwise search interesting pattern discovery first order patterns

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Resource-bounded relational reasoning: Induction and deduction through stochastic matching

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MACHINE LEARNING 2000年第1-2期38卷 41-62页

作者： Sebag, M Rouveirol, C Ecole Polytech LMS CNRS UMR 7649 F-91128 Palaiseau France Univ Paris 11 LRI CNRS UMR 8623 Orsay France

One of the obstacles to widely using first-order logic languages is the fact that relational inference is intractable in the worst case. This paper presents an any-time relational inference algorithm: it proceeds by stochastically sampling the inference search space, after this space has been judiciously restricted using strongly-typed logic-like declarations. We present a relational learner producing programs geared to stochastic inference, named STILL, to enforce the potentialities of this framework. STILL handles examples described as definite or constrained clauses, and uses sampling-based heuristics again to achieve any-time learning. Controlling both the construction and the exploitation of logic programs yields robust relational reasoning, where deductive biases are compensated for by inductive biases, and vice versa.

关键词： first order logic bounded reasoning inductive logic programming

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Bottom-up induction of feature terms

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MACHINE LEARNING 2000年第3期41卷 259-294页

作者： Armengol, E Plaza, E CSIC Artificial Intelligence Res Inst Bellaterra 08193 Catalonia Spain

The aim of relational learning is to develop methods for the induction of hypotheses in representation formalisms that are more expressive than attribute-value representation. Most work on relational learning has been focused on induction in subsets of first order logic like Horn clauses. In this paper we introduce the representation formalism based on feature terms and we introduce the corresponding notions of subsumption and anti-unification. Then we explain INDIE, a heuristic bottom-up learning method that induces class hypotheses, in the form of feature terms, from positive and negative examples. The biases used in INDIE while searching the hypothesis space are explained while describing INDIE's algorithms. The representational bias of INDIE can be summarised in that it makes an intensive use of sorts and sort hierarchy, and in that it does not use negation but focuses on detecting path equalities. We show the results of INDIE in some classical relational datasets showing that it's able to find hypotheses at a level comparable to the original ones. The differences between INDIE's hypotheses and those of the other systems are explained by the bias in searching the hypothesis space and on the representational bias of the hypothesis language of each system.

关键词： inductive logic programming relational learning concept induction feature structures

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PYTHIA-II: A knowledge/database system for managing performance data and recommending scientific software

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ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE 2000年第2期26卷 227-253页

作者： Houstis, EN Catlin, AC Rice, JR Verykios, VS Ramakrishnan, N Houstis, CE Purdue Univ Dept Comp Sci W Lafayette IN 47906 USA Drexel Univ Coll Informat Sci & Technol Philadelphia PA 19104 USA Virginia Polytech Inst & State Univ Dept Comp Sci Blacksburg VA 24061 USA Univ Crete Dept Comp Sci Iraklion Greece

Often scientists need to locate appropriate software for their problems and then select from among many alternatives. We have previously proposed an approach for dealing with this task by processing performance data of the targeted software. This approach has been tested using a customized implementation referred to as PYTHIA. This experience made us realize the complexity of the algorithmic discovery of knowledge from performance data and of the management of these data together with the discovered knowledge. To address this issue, we created PYTHLA-II-a modular framework and system which combines a general knowledge discovery in databases (KDD) methodology and recommender system technologies to provide advice about scientific software/hardware artifacts. The functionality and effectiveness of the system is demonstrated for two existing performance studies using sets of software for solving partial differential equations. From the end-user perspective, PYTHIA-II allows users to specify the problem to be solved and their computational objectives. In turn, PYTHIA-II (i) selects the software available for the user's problem, (ii) suggests parameter values, and (iii) assesses the recommendation provided. PYTHIA-II provides all the necessary facilities to set up database schemas for testing suites and associated performance data in order to test sets of software. Moreover, it allows easy interfacing of alternative data mining and recommendation facilities. PYTHIA-II is an open-ended system implemented on public domain software and has been used for performance evaluation in several different problem domains.

关键词： algorithms experimentation data mining inductive logic programming knowledge-based systems knowledge discovery in databases performance evaluation recommender systems scientific software

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Strategies in combined learning via logic programs

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MACHINE LEARNING 2000年第1-2期38卷 63-87页

作者： Lamma, E Riguzzi, F Pereira, LM Univ Bologna DEIS I-40136 Bologna Italy Univ Nova Lisboa Fac Ciencias & Tecnol Dept Informat Ctr Inteligencia Artificial P-2825 Monte De Caparica Portugal

We discuss the adoption of a three-valued setting for inductive concept learning. Distinguishing between what is true, what is false and what is unknown can be useful in situations where decisions have to be taken on the basis of scarce, ambiguous, or downright contradictory information. In a three-valued setting, we learn a definition for both the target concept and its opposite, considering positive and negative examples as instances of two disjoint classes. To this purpose, we adopt Extended logic Programs (ELP) under a Well-Founded Semantics with explicit negation (WFSX) as the representation formalism for learning, and show how ELPs can be used to specify combinations of strategies in a declarative way also coping with contradiction and exceptions. Explicit negation is used to represent the opposite concept, while default negation is used to ensure consistency and to handle exceptions to general rules. Exceptions are represented by examples covered by the definition for a concept that belong to the training set for the opposite concept. Standard inductive logic programming techniques are employed to learn the concept and its opposite. Depending on the adopted technique, we can learn the most general or the least general definition. Thus, four epistemological varieties occur, resulting from the combination of most general and least general solutions for the positive and negative concept. We discuss the factors that should be taken into account when choosing and strategically combining the generality levels for positive and negative concepts. In the paper, we also handle the issue of strategic combination of possibly contradictory learnt definitions of a predicate and its explicit negation. All in all, we show that extended logic programs under well-founded semantics with explicit negation add expressivity to learning tasks, and allow the tackling of a number of representation and strategic issues in a principled way. Our techniques have been implemented and exam

关键词： inductive logic programming non-monotonic learning multi-strategy learning explicit negation contradiction handling

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