Advances in Artificial Intelligence / 2016 / Article / Fig 1

Research Article

Efficacious Discriminant Analysis (Classifier) Measures for End Users

Figure 1

for each of the common measures are similar. , however, shows that end user results are sensitive to . An end user using receives much more actionable information than if one of the common measures is used.
(a) Each of these commonly seen measures identify similar optimum boundaries. However, the values differ significantly
(b) is tunable to the end user’s situation. End users can readily identify , sensitivity, and the expected impact the classifier will have on their problem