@inbook{d84270ee2e134eb993727a903ee6bc82,
title = "An optimal machine learning classification model for flash memory bit error prediction",
abstract = "NAND flash memory is now almost ubiquitous in the world of data storage. However, NAND wears out as it is used, and manufacturers specify the number of times a device can be rewritten (known as program-erase cycles) very conservatively to account for quality variations within and across devices. This research uses machine learning to predict the true cycling level each part of a NAND device can tolerate, based on measurements taken from the device as it is used. Custom-designed hardware is used to gather millions of data samples and eight machine learning classification methods are compared. The classifier is then optimised using ensemble and knowledge-based techniques. Two new subsampling methods based on the error probability density function are also proposed.",
keywords = "Classification, Classifier ensemble, Error rate prediction, Flash memory, Machine learning, Subsampling",
author = "Barry Fitzgerald and Conor Ryan and Joseph Sullivan",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.",
year = "2019",
doi = "10.1007/978-3-030-02357-7_5",
language = "English",
series = "Studies in Computational Intelligence",
publisher = "Springer-Verlag GmbH and Co. KG",
pages = "89--110",
booktitle = "Studies in Computational Intelligence",
address = "Germany",
}