Using Data Mining for Wine Quality Assessment

By Paulo Cortez, Juliana Teixeira, António Cerdeira, Fernando Almeida, Telmo Matos, José Reis. 2009

62 citations recorded by the source corpus.

Subfields: Food Science, Plant Science, Tourism, Leisure and Hospitality Management

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Food Science

1. Mark J. Embrechts
2. Paulo Cortez

In Plant Science

1. Mark J. Embrechts
2. Paulo Cortez

In Tourism, Leisure and Hospitality Management

1. Mark J. Embrechts
2. Paulo Cortez

Citations in the corpus

• A Survey of Methods for Explaining Black Box Models (2018, neutral): “Then, the visualizations r realized are barplots for the features importance, and Variable Effect Characteristic curve (VEC) [20] plotting the input values (x-axis) versus the (average) outcome responses (see Figure 14(left)).”
• Opening black box Data Mining models using Sensitivity Analysis (2011, methodology): “To demonstrate the visualization of input importances, we selected the wwq-reg dataset, which includes 11 inputs.”
• Data Mining with Neural Networks and Support Vector Machines Using the R/rminer Tool (2010, methodology): “For a more detailed analysis, we propose the variable effect characteristic (VEC) curve [6], which plots the xa,j values (x-axis) versus the ya,j predictions (y-axis).”