Effectiveness of Value Granulation in Machine Learning for Massively Large and Complex Domain
Zachary I Moore, Atsushi Inoue · 2008
Considered from data analysis and dynamic optimization view point, computer networks are massively large and complex data domains where analytical computation (e.g. statistics) is, despite its needs, of-ten found infeasible. In an attempt to address the issues raised by such domains, we are currently study-ing Granular Computing, a newly emerging paradigm, and its appli-cation to Machine Learning. This paper reports effectiveness of value granulation (such as discretization and quantization) in Machine Learn-ing from aspects of complexity re-duction, learning capability, and in-telligent system development.