25,262 research outputs found
Scalable Exact Parent Sets Identification in Bayesian Networks Learning with Apache Spark
In Machine Learning, the parent set identification problem is to find a set
of random variables that best explain selected variable given the data and some
predefined scoring function. This problem is a critical component to structure
learning of Bayesian networks and Markov blankets discovery, and thus has many
practical applications, ranging from fraud detection to clinical decision
support. In this paper, we introduce a new distributed memory approach to the
exact parent sets assignment problem. To achieve scalability, we derive
theoretical bounds to constraint the search space when MDL scoring function is
used, and we reorganize the underlying dynamic programming such that the
computational density is increased and fine-grain synchronization is
eliminated. We then design efficient realization of our approach in the Apache
Spark platform. Through experimental results, we demonstrate that the method
maintains strong scalability on a 500-core standalone Spark cluster, and it can
be used to efficiently process data sets with 70 variables, far beyond the
reach of the currently available solutions
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning
We present a novel hybrid algorithm for Bayesian network structure learning,
called H2PC. It first reconstructs the skeleton of a Bayesian network and then
performs a Bayesian-scoring greedy hill-climbing search to orient the edges.
The algorithm is based on divide-and-conquer constraint-based subroutines to
learn the local structure around a target variable. We conduct two series of
experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is
currently the most powerful state-of-the-art algorithm for Bayesian network
structure learning. First, we use eight well-known Bayesian network benchmarks
with various data sizes to assess the quality of the learned structure returned
by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in
terms of goodness of fit to new data and quality of the network structure with
respect to the true dependence structure of the data. Second, we investigate
H2PC's ability to solve the multi-label learning problem. We provide
theoretical results to characterize and identify graphically the so-called
minimal label powersets that appear as irreducible factors in the joint
distribution under the faithfulness condition. The multi-label learning problem
is then decomposed into a series of multi-class classification problems, where
each multi-class variable encodes a label powerset. H2PC is shown to compare
favorably to MMHC in terms of global classification accuracy over ten
multi-label data sets covering different application domains. Overall, our
experiments support the conclusions that local structural learning with H2PC in
the form of local neighborhood induction is a theoretically well-motivated and
empirically effective learning framework that is well suited to multi-label
learning. The source code (in R) of H2PC as well as all data sets used for the
empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author
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