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

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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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Notes

  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.

  3. The source code for ClustRF is at http://www.eecs.qmul.ac.uk/~xiatian/project_robust_graphs/index.html.

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Authors and Affiliations

  1. School of Science, Engineering and Information Technology, Federation University, Churchill, Australia

    Jonathan R. Wells & Kai Ming Ting

  2. School of Information Technology, Deakin University, Geelong, Australia

    Sunil Aryal

  3. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Kai Ming Ting

Authors
  1. Jonathan R. Wells
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  2. Sunil Aryal
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  3. Kai Ming Ting
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Correspondence to Kai Ming Ting.

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Appendix A: Algorithms to construct isolation forest

Appendix A: Algorithms to construct isolation forest

Algorithms to construct isolation forest and building isolation tree used in \(m_\mathrm{e}\) and \(d_\mathrm{e}\) are provided in Algorithms 1 and 2. Note that after trees are created, the entire dataset D is passed in each isolation tree to record data mass (in \(m_\mathrm{e}\)) and class entropy (in \(d_\mathrm{e}\)) in each node.

figure d
figure e

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Wells, J.R., Aryal, S. & Ting, K.M. Simple supervised dissimilarity measure: Bolstering iForest-induced similarity with class information without learning. Knowl Inf Syst 62, 3203–3216 (2020). https://doi.org/10.1007/s10115-020-01454-3

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