@inproceedings{ed1042f8f13a496fb92f1804bafd2e77,
title = "Soft Sensing as Class-Imbalance Binary Classification – A Lattice Machine Approach",
abstract = "Soft sensing is a class of problems that aim to sense something of interest that cannot be measured directly through something else that can be measured directly. The problems are usually studied as separate topics in different fields, and there is little research studying these problems in a unified fashion. In this paper we argue that there are commonalities among these problems. They can all be formulated as class-imbalanced binary classification problems. We present an extension of Lattice Machine, which is binary classification and by focusing on characterizing positive class to deal with class-imbalanced binary classification problems. We also present experimental results, where some public data sets from UCI data repository are turned into binary-class data and consequently they become class-imbalanced.",
keywords = "Soft Sensing, Class-Imbalance, Binary Classification, Lattice Machine",
author = "Huan Wan and Hui Wang and Gongde Guo and Xin Wei",
note = "Ubiquitous Computing and Ambient Intelligence , UCamI 2014 ; Conference date: 02-12-2014 Through 05-12-2014",
year = "2014",
month = dec,
language = "English",
isbn = "978-3-319-13101-6",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "540--547",
booktitle = "Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services: 8th International Conference, UCAmI 2014, Belfast, UK, December 2-5, 2014. Proceedings",
}