Multiple distribution data description learning algorithm for novelty detection

Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

5 Citations (Scopus)


Current data description learning methods for novelty detection such as support vector data description and small sphere with large margin construct a spherically shaped boundary around a normal data set to separate this set from abnormal data. The volume of this sphere is minimized to reduce the chance of accepting abnormal data. However those learning methods do not guarantee that the single spherically shaped boundary can best describe the normal data set if there exist some distinctive data distributions in this set. We propose in this paper a new data description learning method that constructs a set of spherically shaped boundaries to provide a better data description to the normal data set. An optimisation problem is proposed and solving this problem results in an iterative learning algorithm to determine the set of spherically shaped boundaries. We prove that the classification error will be reduced after each iteration in our learning method. Experimental results on 28 well-known data sets show that the proposed method provides lower classification error rates.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 201, Part II
Number of pages12
ISBN (Print)9783642208461
Publication statusPublished - 2011
Externally publishedYes
EventPacific-Asia Conference on Knowledge Discovery and Data Mining 2011 - Shenzhen, China
Duration: 24 May 201127 May 2011
Conference number: 15th (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 2011
Abbreviated titlePAKDD 2011
Internet address


  • Novelty detection
  • one-class classification
  • spherically shaped boundary
  • support vector data description

Cite this