Explainable Machine Learning for Default Privacy Setting Prediction

Default Privacy Setting Prediction by Grouping User's Attributes and Settings Preferences

Interactive Anonymization for Privacy aware Machine Learning

Privacy aware Machine Learning is the discipline of applying Machine Learning techniques in such a way as to protect and retain personal identities during the process. This is most easily achieved by first anonymizing a dataset before releasing it for the purpose of data mining or knowledge extraction. Starting in June 2018, this will also remain the sole legally permitted way within the EU to ...

Machine Learning for Traffic Prediction

Using machine learning for predicting traffic is described in the context of a competition organized using the TunedIT platform. A heuristic is proposed for reconstructing the route of a car in a street graph from a temporal stream of its coordinates. A resilient propagation neural network for approximating the average velocity on a given street from irregular time series of instantaneous veloc...

Corporate Default Prediction via Deep Learning

This paper provides a new perspective on the default prediction problem using deep learning algorithms. Via the advantages of deep learning, the representable factors of input data will no longer need to be explicitly extracted, but can be implicitly learned by the deep learning algorithms. We consider the stock returns of both default and solvent companies as input signals and adopt one of the...

Transparent Machine Learning Algorithm Offers Useful Prediction Method for Natural Gas Density

Machine-learning algorithms aid predictions for complex systems with multiple influencing variables. However, many neural-network related algorithms behave as black boxes in terms of revealing how the prediction of each data record is performed. This drawback limits their ability to provide detailed insights concerning the workings of the underlying system, or to relate predictions to specific ...