Table of Contents
Advances in Artificial Intelligence
Volume 2013, Article ID 427958, 22 pages
Research Article

Efficacious End User Measures—Part 1: Relative Class Size and End User Problem Domains

Computer Science and Engineering Department, New Mexico Institute of Mining and Technology, 801 Leroy Place, Socorro, NM 87801, USA

Received 29 June 2012; Accepted 28 October 2012

Academic Editor: Konstantinos Lefkimmiatis

Copyright © 2013 E. Earl Eiland and Lorie M. Liebrock. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Biological and medical endeavors are beginning to realize the benefits of artificial intelligence and machine learning. However, classification, prediction, and diagnostic (CPD) errors can cause significant losses, even loss of life. Hence, end users are best served when they have performance information relevant to their needs, this paper’s focus. Relative class size (rCS) is commonly recognized as a confounding factor in CPD evaluation. Unfortunately, rCS-invariant measures are not easily mapped to end user conditions. We determine a cause of rCS invariance, joint probability table (JPT) normalization. JPT normalization means that more end user efficacious measures can be used without sacrificing invariance. An important revelation is that without data normalization, the Matthews correlation coefficient (MCC) and information coefficient (IC) are not relative class size invariants; this is a potential source of confusion, as we found not all reports using MCC or IC normalize their data. We derive MCC rCS-invariant expression. JPT normalization can be extended to allow JPT rCS to be set to any desired value (JPT tuning). This makes sensitivity analysis feasible, a benefit to both applied researchers and practitioners (end users). We apply our findings to two published CPD studies to illustrate how end users benefit.