Modeling Wine Preferences By Data Mining From Physicochemical Properties - Science Assignment Help

Assignment Task

Introduction
Once viewed as a luxury good, nowadays wine is increasingly  enjoyed by a wider range of consumers. Portugal is a top ten wine exporting country, with 3.17% of the market share in 2005 [11]. Exports of its vinho verde wine (from the northwest region) have increased by 36% from 1997 to 2007 [8]. To support its growth, the wine industry is investing in new technologies for both wine making and selling processes. Wine certification and quality assessment are key elements within this context. Certification prevents the illegal adulteration of wines (to safeguard human health) and assures quality for the wine market. Quality evaluation is often part of the certification process and can be used to improve wine making (by identifying the most influential factors) and to stratify wines such as premium brands (useful for setting prices). Wine certification is generally assessed by physicochemical and sensory tests [10]. Physicochemical laboratory tests routinely used to characterize wine include determination of density, alcohol or pH values, while sensory tests rely mainly on human experts. It should be stressed that taste is the least understood of the human senses [25] thus wine classification is a difficult task. Moreover, the relationships between the physicochemical and sensory analysis are complex and still not fully understood [20]. Advances in information technologies have made it possible to collect, store and process massive, often highly complex datasets. All this data hold valuable information such as trends and patterns, which can be used to improve decision making and optimize chances ofsuccess [28]. Data mining (DM) techniques [33] aim at extracting high-level knowledge from raw data. There are several DM algorithms, each one with its own advantages. When modeling continuous data, the linear/multiple regression (MR) is the classic approach. The backpropagation algorithm was first introduced in 1974 [32] and later popularized in 1986 [23]. Since then, neural networks (NNs) have become increasingly used. More recently, support vector machines(SVMs) have also been proposed [4,26]. Due to their higher flexibility and nonlinear learning capabilities, both NNs and SVMs are gaining an attention within the DM field, often attaining high predictive performances [16,17]. SVMs present theoretical advantages over NNs, such as the absence of local minima in the learning phase. In effect, the SVM was recently considered one of the most influential DM algorithms [34]. While the MR model is easier to interpret, it is still possible to extract knowledge from NNs and SVMs, given in terms of input variable importance [18,7]. When applying these DM methods, variable and model selection are critical issues. Variable selection [14] is useful to discard irrelevant inputs, leading to simpler models that are easier to interpret and that usually give better performances. Complex models may overfit the data, losing the capability to generalize, while a model that is too simple will present limited learning capabilities. Indeed, both NN and SVM have hyperparameters that need to be adjusted [16], such as the number of NN hidden nodes or the SVM kernel p

This Science Assignment has been solved by our Science Experts at My Uni Paper. Our Assignment Writing Experts are efficient to provide a fresh solution to this question. We are serving more than 10000+Students in Australia, UK & US by helping them to score HD in their academics. Our Experts are well trained to follow all marking rubrics & referencing style.
    
Be it a used or new solution, the quality of the work submitted by our assignment Experts remains unhampered. You may continue to expect the same or even better quality with the used and new assignment solution files respectively. There’s one thing to be noticed that you could choose one between the two and acquire an HD either way. You could choose a new assignment solution file to get yourself an exclusive, plagiarism (with free Turnitin file), expert quality assignment or order an old solution file that was considered worthy of the highest distinction.