imputation

I am giving a talk “Missing Data Imputation and scikit-multiflow” at the Knowledge Management Group in the University of Auckland in New Zealand.

I am attending the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD18), to present our paper Scalable Model-based Cascaded Imputation of Missing Data. Abstract: Missing data is a common trait of real-world data that can negatively impact interpretability. In this paper, we present CASCADE IMPUTATION (CIM), an effective and scalable technique for automatic imputation of missing data. CIM is not restrictive on the characteristics of the data set, providing support for: Missing At Random and Missing Completely At Random data, numerical and nominal attributes, and large data sets including highly dimensional data sets.