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Knowledge and Information Systems
Published in: Knowledge and Information Systems 8/2020

26-03-2020 | Regular Paper

Simple supervised dissimilarity measure: Bolstering iForest-induced similarity with class information without learning

Authors: Jonathan R. Wells, Sunil Aryal, Kai Ming Ting

Published in: Knowledge and Information Systems | Issue 8/2020

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Abstract

Existing distance metric learning methods require optimisation to learn a feature space to transform data—this makes them computationally expensive in large datasets. In classification tasks, they make use of class information to learn an appropriate feature space. In this paper, we present a simple supervised dissimilarity measure which does not require learning or optimisation. It uses class information to measure dissimilarity of two data instances in the input space directly. It is a supervised version of an existing data-dependent dissimilarity measure called \(m_\mathrm{e}\). Our empirical results in k-NN and LVQ classification tasks show that the proposed simple supervised dissimilarity measure generally produces predictive accuracy better than or at least as good as existing state-of-the-art supervised and unsupervised dissimilarity measures.
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Appendix
Available only for authorised users
Footnotes
1
Use to explain the behaviour of Random Forest, RF similarity aims to track the sign of the margin of x (defined as \(P(+1|x)-P(-1|x)\), where \(+\) 1 and − 1 are the two class labels) [4]. In contrast, iForest-based similarity aims to measure similarity of two points such that points are more similar in sparse region than two points of the same inter-point distance in dense region [22].
 
2
Path length was used by iForest [14] as the anomaly score for the purpose of anomaly detection; and path length is a proxy to mass in mass estimation (see Section 4 in Ting et al. [19]). Mass-based dissimilarity [20], mentioned earlier, is an extension of mass estimation which is implemented using completely random trees such as iForest. Though based on RF, some path length-based similarity [28] can be viewed as a variant of mass-based dissimilarity which is implemented using classification trees rather than completely random trees.
 
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Metadata
Title
Simple supervised dissimilarity measure: Bolstering iForest-induced similarity with class information without learning
Authors
Jonathan R. Wells
Sunil Aryal
Kai Ming Ting
Publication date
26-03-2020
Publisher
Springer London
Published in
Knowledge and Information Systems / Issue 8/2020
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-020-01454-3

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