Partially supervised classification - Based on weighted unlabeled samples support vector machine

Zhigang Liu, Wenzhong Shi, Deren Li, Qianqing Qin

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

19 Citations (Scopus)

Abstract

This paper addresses a new classification technique: partially supervised classification (PSC), which is used to identify a specific land-cover class of interest from a remotely sensed image by using unique training samples belong to a specifically selected class. This paper also presents and discusses a novel Support Vector Machine (SVM) algorithm for PSC. Its training set includes labeled samples belong to the class of interest and unlabeled samples of all classes randomly selected from a remotely sensed image. Moreover, all unlabeled samples are assumed to be training samples of other classes and each of them is assigned a weighting factor indicating the likelihood of this assumption; hence, the algorithm is so-called 'Weighted Unlabeled Sample SVM' (WUS-SVM). Experimental results with both simulated and real data sets indicate that the proposed PSC method is more robust than 1-SVM and has comparable accuracy to a standard SVM.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages118-129
Number of pages12
Publication statusPublished - 1 Dec 2005
Event1st International Conference on Advanced Data Mining and Applications, ADMA 2005 - Wuhan, China
Duration: 22 Jul 200524 Jul 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3584 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Conference on Advanced Data Mining and Applications, ADMA 2005
CountryChina
CityWuhan
Period22/07/0524/07/05

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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