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Automating the analysis of option pricing algorithms through intelligent knowledge acquisition approaches

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2001年第6期31卷 573-586页

作者： Verykios, VS Houstis, EN Tsoukala, LH Pantazopoulos, KN Drexel Univ Coll Informat Sci & Technol Philadelphia PA 19104 USA Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Purdue Univ Sch Nucl Engn W Lafayette IN 47907 USA Goldman Sachs Fixed Income Markets Div London England

The traditional approach for estimating the performance of numerical methods is to combine an operation's count with an asymptotic error analysis. This analytic approach gives a general feel of the comparative efficiency of methods, but it rarely leads to very precise results. It is now recognized that accurate performance evaluation can be made only with actual measurements on working software. Given that such an approach requires an enormous amount of performance data related to actual measurements, the development of novel approaches and systems that intelligently and efficiently analyze these data is of great importance to scientists and engineers. This paper presents new intelligent knowledge acquisition approaches and an integrated prototype system, which enables the automatic and systematic analysis of performance data. The system analyzes the performance data which is usually stored in a database with statistical, and inductive learning techniques and generates knowledge which can be incorporated in a knowledge base incrementally. We demonstrate the use of the system in the context of a case study, covering the analysis of numerical algorithms for the pricing of American vanilla options in a Black and Scholes modeling framework. We also present a qualitative and quantitative comparison of two techniques used for the automated knowledge acquisition phase. Although the system is presented with a particular pricing library in mind, the analysis and evaluation methodology can be used to study algorithms available from other libraries, as long as, these libraries can provide the necessary performance data.

关键词： data mining decision trees inductive logic programming

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Confirmation-guided discovery of first-order rules with Tertius

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MACHINE LEARNING 2001年第1-2期42卷 61-95页

作者： Flach, PA Lachiche, N Univ Bristol Dept Comp Sci Bristol BS8 1TH Avon England

This paper deals with learning first-order logic rules from data lacking an explicit classification predicate. Consequently, the learned rules are not restricted to predicate definitions as in supervised inductive logic programming. First-order logic offers the ability to deal with structured, multi-relational knowledge. Possible applications include first-order knowledge discovery, induction of integrity constraints in databases, multiple predicate learning, and learning mixed theories of predicate definitions and integrity constraints. One of the contributions of our work is a heuristic measure of confirmation, trading off novelty and satisfaction of the rule. The approach has been implemented in the Tertius system. The system performs an optimal best-first search, finding the k most confirmed hypotheses, and includes a non-redundant refinement operator to avoid duplicates in the search. Tertius can be adapted to many different domains by tuning its parameters, and it can deal either with individual-based representations by upgrading propositional representations to first-order, or with general logical rules. We describe a number of experiments demonstrating the feasibility and flexibility of our approach.

关键词： first-order inductive learning inductive logic programming knowledge discovery

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The effect of relational background knowledge on learning of protein three-dimensional fold signatures

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MACHINE LEARNING 2001年第1-2期43卷 81-95页

作者： Turcotte, M Muggleton, SH Sternberg, MJE Imperial Canc Res Fund Biomolec Modelling Lab London WC2A 3PX England Univ York Dept Comp Sci York YO1 5DD N Yorkshire England

As a form of Machine Learning the study of inductive logic programming (ILP) is motivated by a central belief: relational description languages are better tin terms of accuracy and understandability) than propositional ones for certain real-world applications. This claim is investigated here for a particular application in structural molecular biology, that of constructing readable descriptions of the major protein folds. To the authors' knowledge Machine Learning has not previously been applied systematically to this task. In this application, the domain expert (third author) identified a natural divide between essentially propositional features and more structurally-oriented relational ones. The following null hypotheses are tested: 1) for a given ILP system (Progol) provision of relational background knowledge does not increase predictive accuracy, 2) a good propositional learning system (C5.0) without relational background knowledge will outperform Progol with relational background knowledge, 3) relational background knowledge does not produce improved explanatory insight. Null hypotheses 1) and 2) are both refuted on cross-validation results carried out over 20 of the most populated protein folds. Hypothesis 3 is refuted by demonstration of various insightful rules discovered only in the relationally-oriented learned rules.

关键词： inductive logic programming scientific discovery protein fold

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Relational instance-based learning with lists and terms

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MACHINE LEARNING 2001年第1-2期43卷 53-80页

作者： Horváth, T Wrobel, S Bohnebeck, U Univ Magdeburg Sch Comp Sci IWS D-39106 Magdeburg Germany Univ Bremen Ctr Comp Technol D-28834 Bremen Germany

The similarity measures used in first-order IBL so far have been limited to the function-free case. In this paper we show that a lot of power can be gained by allowing lists and other terms in the input representation and designing similarity measures that work directly on these structures. We present an improved similarity measure for the first-order instance-based learner RIBL that employs the concept of edit distances to efficiently compute distances between lists and terms, discuss its computational and formal properties, and empirically demonstrate its additional power on a problem from the domain of biochemistry. The paper also includes a thorough reconstruction of RIBL'S overall algorithm.

关键词： inductive logic programming relational instance-based learning

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Understanding the crucial role of attribute interaction in data mining

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ARTIFICIAL INTELLIGENCE REVIEW 2001年第3期16卷 177-199页

作者： Freitas, AA Pontificia Univ Catolica Parana Postgrad Program Comp Sci BR-80215901 Curitiba Parana Brazil

This is a review paper, whose goal is to significantly improve our understanding of the crucial role of attribute interaction in data mining. The main contributions of this paper are as follows. Firstly, we show that the concept of attribute interaction has a crucial role across different kinds of problem in data mining, such as attribute construction, coping with small disjuncts, induction of first-order logic rules, detection of Simpson's paradox, and finding several types of interesting rules. Hence, a better understanding of attribute interaction can lead to a better understanding of the relationship between these kinds of problems, which are usually studied separately from each other. Secondly, we draw attention to the fact that most rule induction algorithms are based on a greedy search which does not cope well with the problem of attribute interaction, and point out some alternative kinds of rule discovery methods which tend to cope better with this problem. Thirdly, we discussed several algorithms and methods for discovering interesting knowledge that, implicitly or explicitly, are based on the concept of attribute interaction.

关键词： attribute interaction classification constructive induction data mining evolutionary algorithms inductive logic programming rule induction rule interestingness Simpson's paradox small disjuncts

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Approximate match of rules using backpropagation neural networks

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MACHINE LEARNING 2001年第3期44卷 273-299页

作者： Kijsirikul, B Sinthupinyo, S Chongkasemwongse, K Chulalongkorn Univ Dept Comp Engn Bangkok 10330 Thailand

This paper presents a method for approximate match of first-order rules with unseen data. The method is useful especially in case of a multi-class problem or a noisy domain where unseen data are often not covered by the rules. Our method employs the Backpropagation Neural Network for the approximation. To build the network, we propose a technique for generating features from the rules to be used as inputs to the network. Our method has been evaluated on four domains of first-order learning problems. The experimental results show improvements of our method over the use of the original rules. We also applied our method to approximate match of propositional rules converted from an unpruned decision tree. In this case, our method can be thought of as soft-pruning of the decision tree. The results on multi-class learning domains in the UCI repository of machine learning databases show that our method performs better than standard C4.5's pruned and unpruned trees.

关键词： approximate match feature generation inductive logic programming backpropagation neural networks

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Warmr: a data mining tool for chemical data

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JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN 2001年第2期15卷 173-181页

作者： King, RD Srinivasan, A Dehaspe, L Univ Wales Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales Univ Oxford Comp Lab Oxford OX1 3QD England

Data mining techniques are becoming increasingly important in chemistry as databases become too large to examine manually. Data mining methods from the field of inductive logic programming (ILP) have potential advantages for structural chemical data. In this paper we present Warmr, the first ILP data mining algorithm to be applied to chemoinformatic data. We illustrate the value of Warmr by applying it to a well studied database of chemical compounds tested for carcinogenicity in rodents. Data mining was used to find all frequent substructures in the database, and knowledge of these frequent substructures is shown to add value to the database. One use of the frequent substructures was to convert them into probabilistic prediction rules relating compound description to carcinogenesis. These rules were found to be accurate on test data, and to give some insight into the relationship between structure and activity in carcinogenesis. The substructures were also used to prove that there existed no accurate rule, based purely on atom-bond substructure with less than seven conditions, that could predict carcinogenicity. This results put a lower bound on the complexity of the relationship between chemical structure and carcinogenicity. Only by using a data mining algorithm, and by doing a complete search, is it possible to prove such a result. Finally the frequent substructures were shown to add value by increasing the accuracy of statistical and machine learning programs that were trained to predict chemical carcinogenicity. We conclude that Warmr, and ILP data mining methods generally, are an important new tool for analysing chemical databases.

关键词： carcinogenesis chemical structure inductive logic programming machine learning predictive toxicology

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Relational reinforcement learning

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MACHINE LEARNING 2001年第1-2期43卷 7-52页

作者： Dzeroski, S De Raedt, L Driessens, K Jozef Stefan Inst Dept Intelligent Syst SI-1000 Ljubljana Slovenia Univ Freiburg Inst Informat D-79110 Freiburg Germany Katholieke Univ Leuven Dept Comp Sci B-3001 Heverlee Belgium

Relational reinforcement learning is presented, a learning technique that combines reinforcement learning with relational learning or inductive logic programming. Due to the use of a more expressive representation language to represent states, actions and Q-functions, relational reinforcement learning can be potentially applied to a new range of learning tasks. One such task that we investigate is planning in the blocks world, where it is assumed that the effects of the actions are unknown to the agent and the agent has to learn a policy. Within this simple domain we show that relational reinforcement learning solves some existing problems with reinforcement learning. In particular, relational reinforcement learning allows us to employ structural representations, to abstract from specific goals pursued and to exploit the results of previous learning phases when addressing new (more complex) situations.

关键词： reinforcement learning inductive logic programming planning

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Learning logic programs with structured background knowledge

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ARTIFICIAL INTELLIGENCE 2001年第1-2期128卷 31-97页

作者： Horváth, T Turán, C GMD AiS Inst Autonomous Intelligent Syst German Natl Res Ctr Informat Technol D-53754 St Augustin Germany Univ Illinois Dept Math Stat & Comp Sci Chicago IL 60607 USA Hungarian Acad Sci Res Grp Artificial Intelligence Szeged Hungary

The efficient learnability of restricted classes of logic programs is studied in the PAC framework of computational learning theory, We develop the product homomorphism method, which gives polynomial PAC learning algorithms for a nonrecursive Horn clause with function-free ground background knowledge, if the background knowledge satisfies some structural properties. The method is based on a characterization of the concept that corresponds to the relative least general generalization of a set of positive examples with respect to the background knowledge. The characterization is formulated in terms of products and homomorphisms. In the applications this characterization is turned into an explicit combinatorial description, which is then translated into the language of nonrecursive Horn clauses, We show that a nonrecursive Horn clause is polynomially PAC-learnable if there is a single binary background predicate and the ground atoms in the background knowledge form a forest. If the ground atoms in the background knowledge form a disjoint union of cycles then the situation is different, as the shortest consistent hypothesis may have exponential size. In this case polynomial PAC-learnability holds if a different representation language is used. We also consider the complexity of hypothesis finding for multiple clauses in some restricted cases. (C) 2001 Elsevier Science B,V. All rights reserved.

关键词： computational learning theory PAC learning concept learning inductive logic programming VC-dimension

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Ultrasound guided laser ablation of a parathyroid adenoma

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BRITISH JOURNAL OF RADIOLOGY 2001年第886期74卷 905-907页

作者： Bennedbæk, FN Karstrup, S Hegedüs, L Odense Univ Hosp Dept Endocrinol DK-5000 Odense C Denmark Univ Copenhagen Roskilde Hosp Dept Diagnost Imaging DK-4000 Roskilde Denmark

We present a case of primary hyperparathyroidism with severe hypercalcaemia, treated successfully with ultrasound (US) guided percutaneous interstitial laser photocoagulation (ILP) of a single parathyroid tumour. To our knowledge, this is the first reported case of ILP applied in primary hyperparathyroidism. US guided thermic tissue coagulation with ILP may be a nonsurgical alternative in patients with symptomatic hypercalcaemia due to a parathyroid tumour when surgery is contraindicated.

关键词： Adenoma Laser Coagulation Parathyroid Neoplasms 腺瘤激光凝固术甲状旁腺肿瘤 Parathyroid Neoplasms inductive logic programming Laser Ablation primary hyperparathyroidism Laser Coagulation Hypercalcemia Adenoma Ultrasonic Waves Parathyroid Adenoma

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