Machine Learning Approaches and Challenges of Missing Data in the Era of Big Data

  in Special Issue   Posted on November 2, 2017

Information for the Special Issue

Submission Deadline: Sun 31 Dec 2017
Journal Impact Factor : 3.753
Journal Name : International Journal of Machine Learning and Cybernetics
Journal Publisher:
Website for the Special Issue: http://www.springer.com/cda/content/document/cda_downloaddocument/CfP+MLACMDEBD.pdf?SGWID=0-0-45-1614932-p173917603
Journal & Submission Website: https://www.springer.com/journal/13042

Special Issue Call for Papers:

Methods to Evaluate and Understand the Missing Data
Machine Learning Methods to Duplicate Data
Interpolation, Extrapolation, Approximation and other approaches to analyze Missing data
Innovative Learning Techniques to handle Missing Data
Data Imputation and Pairwise deletion and other Techniques of missing data
Historic Learning of Environment and Data Mining for Weather Data
Mobile and Remote Sensing Big Data Evaluation and assessment using Machine Learning
Infrastructure, organizational issues of Machine Learning for Big Data Analytics
Reinforcement Learning for Sustainable and reliable Big Data analytics
AI based Cloud for Big data architectures and real world applications;
Case study , Models, methods, and tools for testing for Missing data