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.
classification visual data mining