Polimoon| Chasers of the Lost Data
Project Details
The Challenge | Chasers of the Lost Data
Help find ways to improve the performance of machine learning and predictive models by filling in gaps in the datasets prior to model training. This entails finding methods to computationally recover or approximate data that is missing due to sensor issues or signal noise that compromises experimental data collection. This work is inspired by data collection during additive manufacturing (AM) processes where sensors capture build characteristics in-situ, but it has applications across many NASA domains.
Filling gaps in datasets
A solution to improve the overall performance of a general ML algorithm.
Traditional methods such as standard imputation do not improve the performance of a general ML algorithm.
We tried alternative approaches that exploit the regularities inside the dataset and we achieved a better accuracy.
We are evaluating to use an approach derived from the recommender system field such as the user rating Netflix problem used to detect the similarity among users to suggest a new film.