7 research outputs found
The IBMAP approach for Markov networks structure learning
In this work we consider the problem of learning the structure of Markov
networks from data. We present an approach for tackling this problem called
IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC
algorithm, designed for avoiding important limitations of existing
independence-based algorithms. These algorithms proceed by performing
statistical independence tests on data, trusting completely the outcome of each
test. In practice tests may be incorrect, resulting in potential cascading
errors and the consequent reduction in the quality of the structures learned.
IBMAP contemplates this uncertainty in the outcome of the tests through a
probabilistic maximum-a-posteriori approach. The approach is instantiated in
the IBMAP-HC algorithm, a structure selection strategy that performs a
polynomial heuristic local search in the space of possible structures. We
present an extensive empirical evaluation on synthetic and real data, showing
that our algorithm outperforms significantly the current independence-based
algorithms, in terms of data efficiency and quality of learned structures, with
equivalent computational complexities. We also show the performance of IBMAP-HC
in a real-world application of knowledge discovery: EDAs, which are
evolutionary algorithms that use structure learning on each generation for
modeling the distribution of populations. The experiments show that when
IBMAP-HC is used to learn the structure, EDAs improve the convergence to the
optimum
Learning Markov networks with context-specific independences
Learning the Markov network structure from data is a problem that has
received considerable attention in machine learning, and in many other
application fields. This work focuses on a particular approach for this purpose
called independence-based learning. Such approach guarantees the learning of
the correct structure efficiently, whenever data is sufficient for representing
the underlying distribution. However, an important issue of such approach is
that the learned structures are encoded in an undirected graph. The problem
with graphs is that they cannot encode some types of independence relations,
such as the context-specific independences. They are a particular case of
conditional independences that is true only for a certain assignment of its
conditioning set, in contrast to conditional independences that must hold for
all its assignments. In this work we present CSPC, an independence-based
algorithm for learning structures that encode context-specific independences,
and encoding them in a log-linear model, instead of a graph. The central idea
of CSPC is combining the theoretical guarantees provided by the
independence-based approach with the benefits of representing complex
structures by using features in a log-linear model. We present experiments in a
synthetic case, showing that CSPC is more accurate than the state-of-the-art IB
algorithms when the underlying distribution contains CSIs.Comment: 8 pages, 6 figure
The Grow-Shrink strategy for learning Markov network structures constrained by context-specific independences
Markov networks are models for compactly representing complex probability
distributions. They are composed by a structure and a set of numerical weights.
The structure qualitatively describes independences in the distribution, which
can be exploited to factorize the distribution into a set of compact functions.
A key application for learning structures from data is to automatically
discover knowledge. In practice, structure learning algorithms focused on
"knowledge discovery" present a limitation: they use a coarse-grained
representation of the structure. As a result, this representation cannot
describe context-specific independences. Very recently, an algorithm called
CSPC was designed to overcome this limitation, but it has a high computational
complexity. This work tries to mitigate this downside presenting CSGS, an
algorithm that uses the Grow-Shrink strategy for reducing unnecessary
computations. On an empirical evaluation, the structures learned by CSGS
achieve competitive accuracies and lower computational complexity with respect
to those obtained by CSPC.Comment: 12 pages, and 8 figures. This works was presented in IBERAMIA 201
Speeding up the execution of a large number of statistical tests of independence
A massive amount of conditional independence tests on data must be performed in the problem of learning the structure of probabilistic graphical models when using the independence-based approach. An intermediate step in the computation of independence tests is the construction of contingency tables from the data. In this work we present an intelligent cache of contingency tables that allows the tables stored to be reused not only for the same test, in the not uncommon case that the test must be performed again, but for an exponential number of other tests, all those involving a subset of the variables of the test stored. In practice, however, not so many tests actually reuse the tables stored. We show results when testing the cache with IBMAP-HC, a recently proposed algorithm for learning the structure of Markov networks, a.k.a. undirected graphical models. The experiments show that in all cases, above 95% of the running time spent by IBMAP-HC in reading data is saved by the cache. The savings in running time for IBMAP-HC were up to 80% for datasets above 40,000 datapoints.Sociedad Argentina de Informática e Investigación Operativ
Efficient comparison of independence structures of log-linear models
Log-linear models are a family of probability distributions which capture a
variety of relationships between variables, including context-specific
independencies. There are a number of approaches for automatic learning of
their independence structures from data, although to date, no efficient method
exists for evaluating these approaches directly in terms of the structures of
the models. The only known methods evaluate these approaches indirectly through
the complete model produced, that includes not only the structure but also the
model parameters, introducing potential distortions in the comparison. This
work presents such a method, that is, a measure for the direct comparison of
the independence structures of log-linear models, inspired by the Hamming
distance comparison method used in undirected graphical models. The measure
presented can be efficiently computed in terms of the number of variables of
the domain, and is proven to be a distance metric