Soft Sensing as Class-Imbalance Binary Classification – A Lattice Machine Approach

Huan Wan, Hui Wang, Gongde Guo, Xin Wei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationUbiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services: 8th International Conference, UCAmI 2014, Belfast, UK, December 2-5, 2014. Proceedings
Place of PublicationSwitzerland
PublisherSpringer
Pages540-547
Number of pages8
ISBN (Electronic)978-3-319-13102-3
ISBN (Print)978-3-319-13101-6
Publication statusPublished - Dec 2014
Externally publishedYes

Publication series

NameLecture Notes in Computer Science
Volume8867
ISSN (Print)0302-9743

Bibliographical note

Ubiquitous Computing and Ambient Intelligence , UCamI 2014 ; Conference date: 02-12-2014 Through 05-12-2014

Keywords

  • Soft Sensing
  • Class-Imbalance
  • Binary Classification
  • Lattice Machine

Cite this