document: Towards exploratory test instance specific algorithms for high dimensional classification In an interactive classification application , a user may find it more valuable to develop a diagnostic decision support method which can reveal significant classification behavior of exemplar records . Such an approach has the additional advantage of being able to optimize the decision process for the individual record in order to design more effective classification methods . In this paper , we propose the Subspace Decision Path method which provides the user with the ability to interactively explore a small number of nodes of a hierarchical decision process so that the most significant classification characteristics for a given test instance are revealed . In addition , the SD-Path method can provide enormous interpretability by constructing views of the data in which the different classes are clearly separated out . Even in cases where the classification behavior of the test instance is ambiguous , the SD-Path method provides a diagnostic understanding of the characteristics which result in this ambiguity . Therefore , this method combines the abilities of the human and the computer in creating an effective diagnostic tool for instance-centered high dimensional classification .
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