TY - JOUR A2 - Keravnou, Elpida AU - Eiland, E. Earl AU - Liebrock, Lorie M. PY - 2016 DA - 2016/01/31 TI - Efficacious Discriminant Analysis (Classifier) Measures for End Users SP - 8173625 VL - 2016 AB - Many problem domains utilize discriminant analysis, for example, classification, prediction, and diagnoses, by applying artificial intelligence and machine learning. However, the results are rarely perfect and errors can cause significant losses. Hence, end users are best served when they have performance information relevant to their need. Starting with the most basic questions, this study considers eight summary statistics often seen in the literature and evaluates their end user efficacy. Results lead to proposed criteria necessary for end user efficacious summary statistics. Testing the same eight summary statistics shows that none satisfy all of the criteria. Hence, two criteria-compliant summary statistics are introduced. To show how end users can benefit, measure utility is demonstrated on two problems. A key finding of this study is that researchers can make their test outcomes more relevant to end users with minor changes in their analyses and presentation. SN - 1687-7470 UR - https://doi.org/10.1155/2016/8173625 DO - 10.1155/2016/8173625 JF - Advances in Artificial Intelligence PB - Hindawi Publishing Corporation KW - ER -