Machine Learning Algorithms for Food Intelligence: Towards a Method for More Accurate Predictions
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It is evident that machine learning algorithms are being widely impacting industrial applications and platforms. Beyond typical research experimentation scenarios, there is a need for companies that wish to enhance their online data and analytics solutions to incorporate ways in which they can select, experiment, benchmark, parameterise and choose the version of a machine learning algorithm that seems to be most appropriate for their specific application context. In this paper, we describe such a need for a big data platform that supports food data analytics and intelligence. More specifically, we introduce Agroknow’s big data platform and identify the need to extend it with a flexible and interactive experimentation environment where different machine learning algorithms can be tested using a variation of synthetic and real data. A typical usage scenario is described, based on our need to experiment with various machine learning algorithms to support price prediction for food products and ingredients. The initial requirements for an experimentation environment are also introduced.
KeywordsMachine learning Deep learning Data analytics Big data Experimentation method
This work is funded with the support by European Commission, and more specifically project Big Data Grapes “Big Data to Enable Global Disruption of the Grapevine-powered industries” (Grant No. 780751) (http://www.bigdatagrapes.eu/), which is funded by the schema “Research and innovation actions (RIA)” under the work programme topic “ICT-16-2017 - Big data PPP: research addressing main technology challenges of the data economy”. This publication reflects the views only of the authors, and the Commission cannot be held responsible for any use, which may be made of the information contained therein.
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