Graph clustering is a popular method to understand networks and make them accessible for downstream Machine Learning tasks. Especially for large networks, the results of graph clustering are difficult to explain, sinc...
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ISBN:
(纸本)9798350307092
Graph clustering is a popular method to understand networks and make them accessible for downstream Machine Learning tasks. Especially for large networks, the results of graph clustering are difficult to explain, since visualizations can be chaotic and the metric used for clustering can be non-interpretable. We propose a method to use inductive logic programming to provide a post-hoc, local explanation for the results of an arbitrary graph clustering algorithm. Our interactive approach is based on three steps: (1) Formalizing graph data as input for the inductive logic programming system Popper, (2) Qualifying entities from the graph as instances for the variables in the resulting formal logic statements and (3) Re-integrating user feedback on the provided, comprehensible statements into Popper. Compared to other benchmark graph clustering explanation approaches, the proposed method shows superior behaviour in terms of interpretability of the rules and clauses as well as in terms of interactivity on six benchmark datasets.
We present a systems biology application of ILP, where the goal is to predict the regulation of a gene under a certain condition from binding site information, the state of regulators, and additional information. In t...
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We present a systems biology application of ILP, where the goal is to predict the regulation of a gene under a certain condition from binding site information, the state of regulators, and additional information. In the experiments, the boosted Tilde model is on par with the original model by Middendorf et al. based on alternating decision trees (ADTrees), given the same information. Adding functional categorizations and protein-protein interactions, however, it is possible to improve the performance substantially. We believe that decoding the regulation mechanisms of genes is an exciting new application of learning in logic, requiring data integration from various sources and potentially contributing to a better understanding on a system level.
Hypotheses constructed by inductive logic programming (ILP) systems are finite sets of definite clauses. Top-down ILP systems usually adopt the following greedy clause-at-a-time strategy to construct such a hypothesis...
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Hypotheses constructed by inductive logic programming (ILP) systems are finite sets of definite clauses. Top-down ILP systems usually adopt the following greedy clause-at-a-time strategy to construct such a hypothesis: start with the empty set of clauses and repeatedly add the clause that most improves the quality of the set. This paper formulates and analyses an alternative method for constructing hypotheses. The method, called cautious induction, consists of a first stage, which finds a finite set of candidate clauses, and a second stage, which selects a finite subset of these clauses to form a hypothesis. By using a less greedy method in the second stage, cautious induction can find hypotheses of higher quality than can be found with a clause-at-a-time algorithm. We have implemented a top-down, cautious ILP system called CILS. This paper presents CILS and compares it to Progol, a top-down clause-at-a-time ILP system. The sizes of the search spaces confronted by the two systems are analysed and an experiment examines their performance on a series of mutagenesis learning problems.
This paper presents a case study of a machine-aided knowledge discovery process within the general area of drug design. Within drug design, the particular problem of pharmacophore discovery is isolated, and the Induct...
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This paper presents a case study of a machine-aided knowledge discovery process within the general area of drug design. Within drug design, the particular problem of pharmacophore discovery is isolated, and the inductive logic programming (ILP) system PROGOL is applied to the problem of identifying potential pharmacophores for ACE inhibition. The case study reported in this paper supports four general lessons for machine learning and knowledge discovery, as well as more specific lessons for pharmacophore discovery, for inductive logic programming, and for ACE inhibition. The general lessons for machine learning and knowledge discovery are as follows. 1. An initial rediscovery step is a useful tool when approaching a new application domain. 2. General machine learning heuristics may fail to match the derails of an application domain, but it may be possible to successfully apply a heuristic-based algorithm in spite of the mismatch. 3. A complete search for all plausible hypotheses can provide useful information to a user, although experimentation may be required to choose between competing hypotheses. 4. A declarative knowledge representation facilitates the development and debugging of background knowledge in collaboration with a domain expert, as well as the communication of final results.
inductive logic programming (ILP) algorithms are classification algorithms that construct classifiers represented as logic programs. ILP algorithms have a number of attractive features, notably the ability to make use...
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inductive logic programming (ILP) algorithms are classification algorithms that construct classifiers represented as logic programs. ILP algorithms have a number of attractive features, notably the ability to make use of declarative background (user-supplied) knowledge. However, ILP algorithms deal poorly with large data sets (> 10(4) examples) and their widespread use of the greedy set-covering algorithm renders them susceptible to local maxima in the space of logic programs. This paper presents a novel approach to address these problems based on combining the local search properties of an inductive logic programming algorithm with the global search properties of an evolutionary algorithm. The proposed algorithm may be viewed as an evolutionary wrapper around a population of ILP algorithms. The evolutionary wrapper approach is evaluated on two domains. The chess-endgame (KRK) problem is an artificial domain that is a widely used benchmark in inductive logic programming, and Part-of-Speech Tagging is a real-world problem from the field of Natural Language Processing. In the latter domain, data originates from excerpts of the Wall Street Journal. Results indicate that significant improvements in predictive accuracy can be achieved over a conventional ILP approach when data is plentiful and noisy.
作者：
Yamamoto, AHokkaido Univ
Div Elect & Informat Engn Kita Ku Sapporo Hokkaido 0608628 Japan Hokkaido Univ
Meme Media Lab Kita Ku Sapporo Hokkaido 0608628 Japan
In this paper we revise Muggleton's theory of inverse entailment, which is the logical foundation of Progol, one of the most famous ILP systems. We first point out that the theory is incomplete in general. Secondl...
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In this paper we revise Muggleton's theory of inverse entailment, which is the logical foundation of Progol, one of the most famous ILP systems. We first point out that the theory is incomplete in general. Secondly we prove that the theory is complete if the background knowledge given to the system is a ground reduced program, every training example is a ground unit clause, and the hypothesis space is the set of all definite clauses. The proof is obtained by showing that every ground reduced logic program is logically equivalent to the conjunction of all atoms in its least Herbrand model. As a corollary to this equivalence, we are finally able to improve the logical foundation of the GOLEM system.
One of the main issues when using inductive logic programming (ILP) in practice remain the long running times that are needed by ILP systems to induce the hypothesis. We explore the possibility of reducing the inducti...
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One of the main issues when using inductive logic programming (ILP) in practice remain the long running times that are needed by ILP systems to induce the hypothesis. We explore the possibility of reducing the induction running times of systems that use asymmetric relative minimal generalisation (ARMG) by analysing the bottom clauses of examples that serve as inputs into the generalisation operator. Using the fact that the ARMG covers all of the examples and that it is a subset of the variabilization of one of the examples, we identify literals that cannot appear in the ARMG and remove them prior to computing the generalisation. We apply this procedure to the ProGolem ILP system and test its performance on several real world data sets. The experimental results show an average speedup of compared to the base ProGolem system and compared to ProGolem extended with caching, both without a decrease in the accuracy of the produced hypotheses. We also observe that the gain from using the proposed method varies greatly, depending on the structure of the data set.
The Variable Precision Rough Set inductive logic programming model (VPRSILP model) extends the Variable Precision Rough Set (VPRS) model to inductive logic programming (ILP). The generic Rough Set inductivelogic Prog...
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The Variable Precision Rough Set inductive logic programming model (VPRSILP model) extends the Variable Precision Rough Set (VPRS) model to inductive logic programming (ILP). The generic Rough Set inductive logic programming (gRS-ILP) model provides a framework for ILP when the setting is imprecise and any induced logic program will not be able to distinguish between certain positive and negative examples. The gRS-ILP model is extended in this paper to the VPRSILP model by including features of the VPRS model. The VPRSILP model is applied to strings and an illustrative experiment on transmembrane domains in amino acid sequences is presented.
This paper presents a methodology to design a discrete-event system (DES) for the on-line supervision of a biotechnological process. The DES is synthesized applying wavelet transform and inductive logic programming on...
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This paper presents a methodology to design a discrete-event system (DES) for the on-line supervision of a biotechnological process. The DES is synthesized applying wavelet transform and inductive logic programming on the measured signals constrained to the biotechnologist expert validation. (C) 2002 Elsevier Science B.V. All rights reserved.
Model transformation by example is a novel approach in model-driven software engineering to derive model transformation rules from an initial prototypical set of interrelated source and target models, which describe c...
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Model transformation by example is a novel approach in model-driven software engineering to derive model transformation rules from an initial prototypical set of interrelated source and target models, which describe critical cases of the model transformation problem in a purely declarative way. In the current paper, we automate this approach using inductive logic programming (Muggleton and Raedt in J logic Program 19-20:629-679, 1994) which aims at the inductive construction of first-order clausal theories from examples and background knowledge.
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