11,311 research outputs found
Disease Outbreaks: Tuning Predictive Machine Learning
Climate change is expected to exacerbate diarrhoea outbreaks in
developing nations, a leading cause of morbidity and mortality in
such regions. The development of predictive models with the
ability to capture complex relationships between climate factors and
diarrhoea may be effective for diarrhoea outbreak control. Various
supervised Machine Learning (ML) algorithms and Deep Learning
(DL) methods have been used in developing predictive models for
various disease. Despite their advances in a range of healthcare applications, overall method task performance still largely
depends on available training data and parameter settings which is
a significant challenge for most predictive machine learning methods. This study investigates the impact of Relevance Estimation
and Value Calibration (REVAC), an evolutionary parameter optimization method applied to predictive task performance of various
ML and DL methods applied to ranges of real-world and synthetic
data-sets (diarrhoea and climate based) for daily diarrhoea outbreak
prediction in a regional case-study (South African provinces). Preliminary results indicate that REVAC is better suited for the DL
models regardless of the data-set used for making predictions
Efficient learning in ABC algorithms
Approximate Bayesian Computation has been successfully used in population
genetics to bypass the calculation of the likelihood. These methods provide
accurate estimates of the posterior distribution by comparing the observed
dataset to a sample of datasets simulated from the model. Although
parallelization is easily achieved, computation times for ensuring a suitable
approximation quality of the posterior distribution are still high. To
alleviate the computational burden, we propose an adaptive, sequential
algorithm that runs faster than other ABC algorithms but maintains accuracy of
the approximation. This proposal relies on the sequential Monte Carlo sampler
of Del Moral et al. (2012) but is calibrated to reduce the number of
simulations from the model. The paper concludes with numerical experiments on a
toy example and on a population genetic study of Apis mellifera, where our
algorithm was shown to be faster than traditional ABC schemes
Comparing parameter tuning methods for evolutionary algorithms
Abstract — Tuning the parameters of an evolutionary algorithm (EA) to a given problem at hand is essential for good algorithm performance. Optimizing parameter values is, however, a non-trivial problem, beyond the limits of human problem solving.In this light it is odd that no parameter tuning algorithms are used widely in evolutionary computing. This paper is meant to be stepping stone towards a better practice by discussing the most important issues related to tuning EA parameters, describing a number of existing tuning methods, and presenting a modest experimental comparison among them. The paper is concluded by suggestions for future research – hopefully inspiring fellow researchers for further work. Index Terms — evolutionary algorithms, parameter tuning I. BACKGROUND AND OBJECTIVES Evolutionary Algorithms (EA) form a rich class of stochasti
Costs and benefits of tuning parameters of evolutionary algorithms
Abstract. We present an empirical study on the impact of different design choices on the performance of an evolutionary algorithm (EA). Four EA components are considered—parent selection, survivor selection, recombination and mutation—and for each component we study the impact of choosing the right operator and of tuning its free parameter(s). We tune 120 different combinations of EA operators to 4 different classes of fitness landscapes and measure the cost of tuning. We find that components differ greatly in importance. Typically the choice of operator for parent selection has the greatest impact, and mutation needs the most tuning. Regarding individual EAs however, the impact of design choices for one component depends on the choices for other components, as well as on the available amount of resources for tuning.
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