Multiple distribution data description learning algorithm for novelty detection

Authors:
Trung Le;Dat Tran;Wanli Ma;Dharmendra Sharma
Affiliations:
Faculty of Information Sciences and Engineering, University of Canberra, ACT, Australia;Faculty of Information Sciences and Engineering, University of Canberra, ACT, Australia;Faculty of Information Sciences and Engineering, University of Canberra, ACT, Australia;Faculty of Information Sciences and Engineering, University of Canberra, ACT, Australia
Venue:
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Year:
2011

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Abstract

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.