Background: The COVID-19 pandemic affects all aspects of human life including their food consumption. The changes in the food production and supply processes introduce changes to the global dietary patterns. Scope and Approach: To study the COVID-19 impact on food consumption process, we have analyzed two data sets that consist of food preparation recipes published before (69,444) and during the quarantine (10,009) period. Since working with large data sets is a time-consuming task, we have applied a recently proposed artificial intelligence approach called DietHub. The approach uses the recipe preparation description (i.e. text) and automatically provides a list of main ingredients annotated using the Hansard semantic tags. After extracting the semantic tags of the ingredients for every recipe, we have compared the food consumption patterns between the two data sets by comparing the relative frequency of the ingredients that compose the recipes. Key Findings and Conclusions: Using the AI methodology, the changes in the food consumption patterns before and during the COVID-19 pandemic are obvious. The highest positive difference in the food consumption can be found in foods such as “Pulses/ plants producing pulses”, “Pancake/Tortilla/Outcake”, and “Soup/pottage”, which increase by 300%, 280%, and 100%, respectively. Conversely, the largest decrease in consumption can be food for food such as “Order Perciformes (type of fish)”, “Corn/cereals/grain”, and “Wine-making”, with a reduction of 50%, 40%, and 30%, respectively. This kind of analysis is valuable in times of crisis and emergencies, which is a very good example of the scientific support that regulators require in order to take quick and appropriate response.
In this work, we address the task of feature ranking for multi-target regression (MTR). The task of MTR concerns problems with multiple continuous dependent/target variables, where the goal is to learn a model for predicting all of them simultaneously. This task is receiving an increasing attention from the research community, but performing feature ranking in the context of MTR has not been studied thus far. Here, we study two groups of feature ranking scores for MTR: scores (Symbolic, Genie3 and Random Forest score) based on ensembles (bagging, random forests, extra trees) of predictive clustering trees, and a score derived as an extension of the RReliefF method. We also propose a generic data-transformation approach to MTR feature ranking and thus have two versions of each score. For both groups of feature ranking scores, we analyze their theoretical computational complexity. For the extension of the RReliefF method, we additionally derive some theoretical properties of the scores. Next, we extensively evaluate the scores on 24 benchmark MTR datasets, in terms of the quality of the ranking and the computational complexity of producing it. The results identify the parameters that influence the quality of the rankings, reveal that both groups of methods produce relevant feature rankings, and show that the Symbolic and Genie3 score, coupled with random forest ensembles, yield the best rankings. Keywords Feature ranking • Multi target regression • Tree based methods • Relief 1 Introduction Single target regression (STR) is the predictive modeling task of learning a model able to predict the values of a single numeric target variable. STR can be generalized to multi-target
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