48,125 research outputs found
Is One Hyperparameter Optimizer Enough?
Hyperparameter tuning is the black art of automatically finding a good
combination of control parameters for a data miner. While widely applied in
empirical Software Engineering, there has not been much discussion on which
hyperparameter tuner is best for software analytics. To address this gap in the
literature, this paper applied a range of hyperparameter optimizers (grid
search, random search, differential evolution, and Bayesian optimization) to
defect prediction problem. Surprisingly, no hyperparameter optimizer was
observed to be `best' and, for one of the two evaluation measures studied here
(F-measure), hyperparameter optimization, in 50\% cases, was no better than
using default configurations.
We conclude that hyperparameter optimization is more nuanced than previously
believed. While such optimization can certainly lead to large improvements in
the performance of classifiers used in software analytics, it remains to be
seen which specific optimizers should be applied to a new dataset.Comment: 7 pages, 2 columns, accepted for SWAN1
Evolving artificial datasets to improve interpretable classifiers
Differential Evolution can be used to construct effective and compact artificial training datasets for machine learning algorithms. In this paper, a series of comparative experiments are performed in which two simple interpretable supervised classifiers (specifically, Naive Bayes and linear Support Vector Machines) are trained (i) directly on “real” data, as would be the normal case, and (ii) indirectly, using special artificial datasets derived from real data via evolutionary optimization. The results across several challenging test problems show that supervised classifiers trained indirectly using our novel evolution-based approach produce models with superior predictive classification performance. Besides presenting the accuracy of the learned models, we also analyze the sensitivity of our artificial data optimization process to Differential Evolution's parameters, and then we examine the statistical characteristics of the artificial data that is evolved
Learning and Designing Stochastic Processes from Logical Constraints
Stochastic processes offer a flexible mathematical formalism to model and
reason about systems. Most analysis tools, however, start from the premises
that models are fully specified, so that any parameters controlling the
system's dynamics must be known exactly. As this is seldom the case, many
methods have been devised over the last decade to infer (learn) such parameters
from observations of the state of the system. In this paper, we depart from
this approach by assuming that our observations are {\it qualitative}
properties encoded as satisfaction of linear temporal logic formulae, as
opposed to quantitative observations of the state of the system. An important
feature of this approach is that it unifies naturally the system identification
and the system design problems, where the properties, instead of observations,
represent requirements to be satisfied. We develop a principled statistical
estimation procedure based on maximising the likelihood of the system's
parameters, using recent ideas from statistical machine learning. We
demonstrate the efficacy and broad applicability of our method on a range of
simple but non-trivial examples, including rumour spreading in social networks
and hybrid models of gene regulation
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