Automated Data Cleansing through Meta-Learning

Innovative Applications of Artificial Intelligence (IAAI)

Published February 4, 2017

Ian Gemp, Georgios Theocharous, Mohammad Ghavamzadeh

Data preprocessing or cleansing is one of the biggest hurdles in industry for developing successful machine learning appli- cations. The process of data cleansing includes data imputa- tion, feature normalization & selection, dimensionality reduc- tion, and data balancing applications. Currently such prepro- cessing is manual. One approach for automating this process is meta-learning. In this paper we experiment with state of the art meta-learning methodologies and identify the inade- quacies and research challenges for solving such a problem.

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Research Area:  AI & Machine Learning