NDLI: Towards exploratory test instance specific algorithms for high dimensional classification

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Towards exploratory test instance specific algorithms for high dimensional classification

Content Provider	ACM Digital Library
Author	Aggarwal, Charu C.
Abstract	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.
Starting Page	526
Ending Page	531
Page Count	6
File Format	PDF
ISBN	159593135X
DOI	10.1145/1081870.1081931
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-08-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Classification Visual data mining
Content Type	Text
Resource Type	Article

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