Data Simulation in Machine Olfaction with the R Package Chemosensors

Andrey Ziyatdinov and Alexandre Perera-Lluna

PLOS ONE, 2014, vol. 9, issue 2, 1-19

Abstract: In machine olfaction, the design of applications based on gas sensor arrays is highly dependent on the robustness of the signal and data processing algorithms. While the practice of testing the algorithms on public benchmarks is not common in the field, we propose software for performing data simulations in the machine olfaction field by generating parameterized sensor array data. The software is implemented as an R language package chemosensors which is open-access, platform-independent and self-contained. We introduce the concept of a virtual sensor array which can be used as a data generation tool. In this work, we describe the data simulation workflow which basically consists of scenario definition, virtual array parameterization and the generation of sensor array data. We also give examples of the processing of the simulated data as proof of concept for the parameterized sensor array data: the benchmarking of classification algorithms, the evaluation of linear- and non-linear regression algorithms, and the biologically inspired processing of sensor array data. All the results presented were obtained under version 0.7.6 of the chemosensors package whose home page is chemosensors.r-forge.r-project.org.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0088839

DOI: 10.1371/journal.pone.0088839

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