Efficacious End User Measures—Part 1 : Relative Class Size and End User Problem Domains
Joint Authors
Eiland, E. Earl
Liebrock, Lorie M.
Source
Advances in Artificial Intelligence
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-22, 22 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-02-26
Country of Publication
Egypt
No. of Pages
22
Main Subjects
Information Technology and Computer Science
Science
Abstract EN
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.
American Psychological Association (APA)
Eiland, E. Earl& Liebrock, Lorie M.. 2013. Efficacious End User Measures—Part 1 : Relative Class Size and End User Problem Domains. Advances in Artificial Intelligence،Vol. 2013, no. 2013, pp.1-22.
https://search.emarefa.net/detail/BIM-471422
Modern Language Association (MLA)
Eiland, E. Earl& Liebrock, Lorie M.. Efficacious End User Measures—Part 1 : Relative Class Size and End User Problem Domains. Advances in Artificial Intelligence No. 2013 (2013), pp.1-22.
https://search.emarefa.net/detail/BIM-471422
American Medical Association (AMA)
Eiland, E. Earl& Liebrock, Lorie M.. Efficacious End User Measures—Part 1 : Relative Class Size and End User Problem Domains. Advances in Artificial Intelligence. 2013. Vol. 2013, no. 2013, pp.1-22.
https://search.emarefa.net/detail/BIM-471422
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-471422