Publications

@incollection{alaiz-rodriguez_visualizing_2009,

title = {Visualizing High Dimensional Classifier Performance Data},

author = {Rocio Alaiz-Rodríguez and Nathalie Japkowicz and Peter Tischer},

url = {https://link.springer.com/chapter/10.1007/978-3-642-02190-9_6},

year  = {2009},

date = {2009-01-01},

booktitle = {Advances in Data Management},

pages = {105–129},

publisher = {Springer Berlin Heidelberg Berlin, Heidelberg},

abstract = {This paper addresses classifier performance evaluation, emphasizing the importance of analyzing high-dimensional data. By using visual representations of evaluation data, the approach leverages human visual capabilities to gain insights, interact with, and draw meaningful conclusions about classifiers and domains. The paper demonstrates how projection techniques from high-dimensional to lower-dimensional spaces enable exploratory analysis. This method is presented as a generalization of traditional evaluation methods based on point metrics, offering a more informative and interactive evaluation process. The framework allows users to study data from both a classifier and domain perspective, which is not possible with conventional methods.},

keywords = {classifier evaluation, High-dimensional data, Visual Representation},

pubstate = {published},

tppubtype = {incollection}

}