ML Anthology

Decision Region Connectivity Analysis: A Method for Analyzing High-Dimensional Classifiers

Ofer Melnik
MLJ 2002 pp. 321-351
doi:10.1023/A:1013968124284 /mlj/2002/melnik2002mlj-decision/

Abstract

In this paper we present a method to extract qualitative information from any classification model that uses decision regions to generalize (e.g., feed-forward neural nets, SVMs, etc). The method's complexity is independent of the dimensionality of the input data or model, making it computationally feasible for the analysis of even very high-dimensional models. The qualitative information extracted by the method can be directly used to analyze the classification strategies employed by a model, and also to compare strategies across different model types.

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Text

Melnik. "Decision Region Connectivity Analysis: A Method for Analyzing High-Dimensional Classifiers." Machine Learning, 2002. doi:10.1023/A:1013968124284

Markdown

[Melnik. "Decision Region Connectivity Analysis: A Method for Analyzing High-Dimensional Classifiers." Machine Learning, 2002.](https://mlanthology.org/mlj/2002/melnik2002mlj-decision/) doi:10.1023/A:1013968124284

BibTeX

@article{melnik2002mlj-decision,
  title     = {{Decision Region Connectivity Analysis: A Method for Analyzing High-Dimensional Classifiers}},
  author    = {Melnik, Ofer},
  journal   = {Machine Learning},
  year      = {2002},
  pages     = {321-351},
  doi       = {10.1023/A:1013968124284},
  volume    = {48},
  url       = {https://mlanthology.org/mlj/2002/melnik2002mlj-decision/}
}