Premium
Inference enterprise models: An approach to organizational performance improvement
Author(s) -
Buede Dennis M.,
Axelrad Elise T.,
Brown David P.,
Hudson Daniel W.,
Laskey Kathryn B.,
Sticha Paul J.,
Thomas Jordan L.
Publication year - 2018
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1277
Subject(s) - inference , computer science , insider , set (abstract data type) , data science , artificial intelligence , process (computing) , competition (biology) , machine learning , broad spectrum , management science , knowledge management , engineering , ecology , chemistry , political science , law , combinatorial chemistry , biology , programming language , operating system
We demonstrate that our success in solving a set of increasingly complex challenge problems is associated with an inference enterprise (IE) using inference enterprise models (IEMs). As part of a sponsored research competition, we created a multimodeling inference enterprise modeling (MIEM) process to achieve winning scores on a spectrum of challenge problems related to insider threat detection. We present in general terms the motivation for and description of our MIEM solution. We then present the results of applying MIEM across a range of challenge problems, with a detailed illustration for one challenge problem. Finally, we discuss the science and promise of IEM and MIEM, including the applicability of MIEM to a spectrum of inference domains. This article is categorized under: Technologies > Machine Learning Algorithmic Development > Ensemble Methods Technologies > Prediction