DAO 2017 : Data Analytics and Optimization
Call For Papers
The interplay between Optimization and Machine Learning is a fundamental step towards the modern decision sciences in the era of Big Data.
Machine learning researchers have exploited optimization models and algorithms to build automatic knowledge discovery from data while optimization approaches have latch on to machine learning because of their wide applicability and attractive theoretical properties.
The increasing complexity, size, and variety of today’s data analytics models presents new challenges for mathematical programming and optimization, and calls for both new development and enriched resurgence of operations research methods.
Optimization models and algorithms can improve the data-driven decision models by either formulating the pattern discovery and knowledge extraction problems or defining efficient algorithms for enabling machine learning at a massive scale.
This Special issue on “Data Analytics and Optimization” aims at gathering the ongoing cross-disciplinary research by involving operation research, machine learning and statistical methods to extract essential knowledge from huge volumes of data.
*Topics of Interest*
Optimization methods for machine learning
Analytics and Intelligent Optimization
Fuzzy Optimization in machine learning
Graphs and Networks Analytics
Algorithms for statistical model learning
Mathematical problem formulations for learning and inference
These and other related methodological, theoretical, and empirical contributions are all welcome.
Submission start: February 15th, 2017
Paper submission deadline (extended): July 15th, 2017
Notification of Review Results: September 30th, 2017
Submission deadline for papers invited for revision: November 30th, 2017
Final decision of acceptance: December 30th, 2017
Desirable publication date: March 2018
Elisabetta Fersini (Email - firstname.lastname@example.org)
Francesca Guerriero (Email - email@example.com)
Enza Messina (Email - firstname.lastname@example.org)
Daniele Vigo (Email - email@example.com)