1,997 research outputs found
Qualitative System Identification from Imperfect Data
Experience in the physical sciences suggests that the only realistic means of
understanding complex systems is through the use of mathematical models.
Typically, this has come to mean the identification of quantitative models
expressed as differential equations. Quantitative modelling works best when the
structure of the model (i.e., the form of the equations) is known; and the
primary concern is one of estimating the values of the parameters in the model.
For complex biological systems, the model-structure is rarely known and the
modeler has to deal with both model-identification and parameter-estimation. In
this paper we are concerned with providing automated assistance to the first of
these problems. Specifically, we examine the identification by machine of the
structural relationships between experimentally observed variables. These
relationship will be expressed in the form of qualitative abstractions of a
quantitative model. Such qualitative models may not only provide clues to the
precise quantitative model, but also assist in understanding the essence of
that model. Our position in this paper is that background knowledge
incorporating system modelling principles can be used to constrain effectively
the set of good qualitative models. Utilising the model-identification
framework provided by Inductive Logic Programming (ILP) we present empirical
support for this position using a series of increasingly complex artificial
datasets. The results are obtained with qualitative and quantitative data
subject to varying amounts of noise and different degrees of sparsity. The
results also point to the presence of a set of qualitative states, which we
term kernel subsets, that may be necessary for a qualitative model-learner to
learn correct models. We demonstrate scalability of the method to biological
system modelling by identification of the glycolysis metabolic pathway from
data
Schema Independent Relational Learning
Learning novel concepts and relations from relational databases is an
important problem with many applications in database systems and machine
learning. Relational learning algorithms learn the definition of a new relation
in terms of existing relations in the database. Nevertheless, the same data set
may be represented under different schemas for various reasons, such as
efficiency, data quality, and usability. Unfortunately, the output of current
relational learning algorithms tends to vary quite substantially over the
choice of schema, both in terms of learning accuracy and efficiency. This
variation complicates their off-the-shelf application. In this paper, we
introduce and formalize the property of schema independence of relational
learning algorithms, and study both the theoretical and empirical dependence of
existing algorithms on the common class of (de) composition schema
transformations. We study both sample-based learning algorithms, which learn
from sets of labeled examples, and query-based algorithms, which learn by
asking queries to an oracle. We prove that current relational learning
algorithms are generally not schema independent. For query-based learning
algorithms we show that the (de) composition transformations influence their
query complexity. We propose Castor, a sample-based relational learning
algorithm that achieves schema independence by leveraging data dependencies. We
support the theoretical results with an empirical study that demonstrates the
schema dependence/independence of several algorithms on existing benchmark and
real-world datasets under (de) compositions
Logic-based machine learning using a bounded hypothesis space: the lattice structure, refinement operators and a genetic algorithm approach
Rich representation inherited from computational logic makes logic-based machine learning a competent method for application domains involving relational background knowledge and structured data. There is however a trade-off between the expressive power of the representation and the computational costs. Inductive Logic Programming (ILP) systems employ different kind of biases and heuristics to cope with the complexity of the search, which otherwise is intractable. Searching the hypothesis space bounded below by a bottom clause is the basis of several state-of-the-art ILP systems (e.g. Progol and Aleph). However, the structure of the search space and the properties of the refinement operators for theses systems have not been previously characterised. The contributions of this thesis can be summarised as follows: (i) characterising the properties, structure and morphisms of bounded subsumption lattice (ii) analysis of bounded refinement operators and stochastic refinement and (iii) implementation and empirical evaluation of stochastic search algorithms and in particular a Genetic Algorithm (GA) approach for bounded subsumption. In this thesis we introduce the concept of bounded subsumption and study the lattice and cover structure of bounded subsumption. We show the morphisms between the lattice of bounded subsumption, an atomic lattice and the lattice of partitions. We also show that ideal refinement operators exist for bounded subsumption and that, by contrast with general subsumption, efficient least and minimal generalisation operators can be designed for bounded subsumption. In this thesis we also show how refinement operators can be adapted for a stochastic search and give an analysis of refinement operators within the framework of stochastic refinement search. We also discuss genetic search for learning first-order clauses and describe a framework for genetic and stochastic refinement search for bounded subsumption. on. Finally, ILP algorithms and implementations which are based on this framework are described and evaluated.Open Acces
Efficient Learning and Evaluation of Complex Concepts in Inductive Logic Programming
Inductive Logic Programming (ILP) is a subfield of Machine Learning with foundations in logic
programming. In ILP, logic programming, a subset of first-order logic, is used as a uniform
representation language for the problem specification and induced theories. ILP has been
successfully applied to many real-world problems, especially in the biological domain (e.g. drug
design, protein structure prediction), where relational information is of particular importance.
The expressiveness of logic programs grants flexibility in specifying the learning task and understandability
to the induced theories. However, this flexibility comes at a high computational
cost, constraining the applicability of ILP systems. Constructing and evaluating complex concepts
remain two of the main issues that prevent ILP systems from tackling many learning
problems. These learning problems are interesting both from a research perspective, as they
raise the standards for ILP systems, and from an application perspective, where these target
concepts naturally occur in many real-world applications. Such complex concepts cannot
be constructed or evaluated by parallelizing existing top-down ILP systems or improving the
underlying Prolog engine. Novel search strategies and cover algorithms are needed.
The main focus of this thesis is on how to efficiently construct and evaluate complex hypotheses
in an ILP setting. In order to construct such hypotheses we investigate two approaches.
The first, the Top Directed Hypothesis Derivation framework, implemented in the ILP system
TopLog, involves the use of a top theory to constrain the hypothesis space. In the second approach
we revisit the bottom-up search strategy of Golem, lifting its restriction on determinate
clauses which had rendered Golem inapplicable to many key areas. These developments led to
the bottom-up ILP system ProGolem. A challenge that arises with a bottom-up approach is the
coverage computation of long, non-determinate, clauses. Prolog’s SLD-resolution is no longer
adequate. We developed a new, Prolog-based, theta-subsumption engine which is significantly
more efficient than SLD-resolution in computing the coverage of such complex clauses.
We provide evidence that ProGolem achieves the goal of learning complex concepts by presenting
a protein-hexose binding prediction application. The theory ProGolem induced has
a statistically significant better predictive accuracy than that of other learners. More importantly,
the biological insights ProGolem’s theory provided were judged by domain experts to
be relevant and, in some cases, novel
LogCHEM: interactive discriminative mining of chemical structure
One of the most well known successes of Inductive Logic Programming (ILP) is on Structure-Activity Relationship (SAR) problems. In such problems, ILP has proved several times to be capable of constructing expert comprehensible models that hell) to explain the activity of chemical compounds based on their structure and properties. However, despite its successes on SAR problems, ILP has severe scalability problems that prevent its application oil larger datasets. In this paper we present LogCHEM, an ILP based tool for discriminative interactive mining of chemical fragments. LogCHEM tackles ILP's scalability issues in the context of SAR applications. We show that LogCHEM benefits from the flexibility of ILP both by its ability to quickly extend the original mining model, and by its ability, to interface with external tools. Furthermore, We demonstrate that LogCHEM can be used to mine effectively large chemoinformatics datasets, namely, several datasets from EPA's DSSTox database and on a dataset based on the DTP AIDS anti-viral screen
Learning programs by learning from failures
We describe an inductive logic programming (ILP) approach called learning
from failures. In this approach, an ILP system (the learner) decomposes the
learning problem into three separate stages: generate, test, and constrain. In
the generate stage, the learner generates a hypothesis (a logic program) that
satisfies a set of hypothesis constraints (constraints on the syntactic form of
hypotheses). In the test stage, the learner tests the hypothesis against
training examples. A hypothesis fails when it does not entail all the positive
examples or entails a negative example. If a hypothesis fails, then, in the
constrain stage, the learner learns constraints from the failed hypothesis to
prune the hypothesis space, i.e. to constrain subsequent hypothesis generation.
For instance, if a hypothesis is too general (entails a negative example), the
constraints prune generalisations of the hypothesis. If a hypothesis is too
specific (does not entail all the positive examples), the constraints prune
specialisations of the hypothesis. This loop repeats until either (i) the
learner finds a hypothesis that entails all the positive and none of the
negative examples, or (ii) there are no more hypotheses to test. We introduce
Popper, an ILP system that implements this approach by combining answer set
programming and Prolog. Popper supports infinite problem domains, reasoning
about lists and numbers, learning textually minimal programs, and learning
recursive programs. Our experimental results on three domains (toy game
problems, robot strategies, and list transformations) show that (i) constraints
drastically improve learning performance, and (ii) Popper can outperform
existing ILP systems, both in terms of predictive accuracies and learning
times.Comment: Accepted for the machine learning journa
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