175,735 research outputs found
Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME
We present a heuristic based algorithm to induce \textit{nonmonotonic} logic
programs that will explain the behavior of XGBoost trained classifiers. We use
the technique based on the LIME approach to locally select the most important
features contributing to the classification decision. Then, in order to explain
the model's global behavior, we propose the LIME-FOLD algorithm ---a
heuristic-based inductive logic programming (ILP) algorithm capable of learning
non-monotonic logic programs---that we apply to a transformed dataset produced
by LIME. Our proposed approach is agnostic to the choice of the ILP algorithm.
Our experiments with UCI standard benchmarks suggest a significant improvement
in terms of classification evaluation metrics. Meanwhile, the number of induced
rules dramatically decreases compared to ALEPH, a state-of-the-art ILP system
Heuristic Classification of Physical Theories based on Quantum Correlations
Taking quantum formalism as a point of reference and connection, we explore
the various possibilities that arise in the construction of physical theories.
Analyzing the distinct physical phenomena that each of them may describe, we
introduce the different types of hidden variables theories that correspond to
these physical phenomena. A hierarchical classification of the offered
theories, based on the degree of correlation between dichotomic observables in
bipartite systems, as quantified by a Bell type inequality, is finally
proposed.Comment: 13 pages, 2 figure
Inferring heuristic classification hierarchies from natural language input
A methodology for inferring hierarchies representing heuristic knowledge about the check out, control, and monitoring sub-system (CCMS) of the space shuttle launch processing system from natural language input is explained. Our method identifies failures explicitly and implicitly described in natural language by domain experts and uses those descriptions to recommend classifications for inclusion in the experts' heuristic hierarchies
A New Heuristic for Feature Selection by Consistent Biclustering
Given a set of data, biclustering aims at finding simultaneous partitions in
biclusters of its samples and of the features which are used for representing
the samples. Consistent biclusterings allow to obtain correct classifications
of the samples from the known classification of the features, and vice versa,
and they are very useful for performing supervised classifications. The problem
of finding consistent biclusterings can be seen as a feature selection problem,
where the features that are not relevant for classification purposes are
removed from the set of data, while the total number of features is maximized
in order to preserve information. This feature selection problem can be
formulated as a linear fractional 0-1 optimization problem. We propose a
reformulation of this problem as a bilevel optimization problem, and we present
a heuristic algorithm for an efficient solution of the reformulated problem.
Computational experiments show that the presented algorithm is able to find
better solutions with respect to the ones obtained by employing previously
presented heuristic algorithms
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