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Visual Human+Machine Learning
November/December 2009 (vol. 15 no. 6)
pp. 1327-1334
ASCII Text
x 
 
Raphael Fuchs, Jürgen Waser, Meister Eduard Gröller, "Visual Human+Machine Learning," IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 6, pp. 1327-1334, November/December, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TVCG.2009.199,
author = {Raphael Fuchs and Jürgen Waser and Meister Eduard Gröller},
title = {Visual Human+Machine Learning},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {15},
number = {6},
issn = {1077-2626},
year = {2009},
pages = {1327-1334},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.199},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - Visual Human+Machine Learning
IS - 6
SN - 1077-2626
SP1327
EP1334
EPD - 1327-1334
A1 - Raphael Fuchs,
A1 - Jürgen Waser,
A1 - Meister Eduard Gröller,
PY - 2009
KW - Interactive Visual Analysis
KW - Volumetric Data
KW - Multiple Competing Hypotheses
KW - Knowledge Discovery
KW - Computer-assisted Multivariate Data Exploration
KW - Curse of Dimensionality
KW - Predictive Analysis
KW - Genetic Algorithm
VL - 15
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
Raphael Fuchs, ETH Zurich
Jürgen Waser, VRVis Vienna
In this paper we describe a novel method to integrate interactive visual analysis and machine learning to support the insight generation of the user. The suggested approach combines the vast search and processing power of the computer with the superior reasoning and pattern recognition capabilities of the human user. An evolutionary search algorithm has been adapted to assist in the fuzzy logic formalization of hypotheses that aim at explaining features inside multivariate, volumetric data. Up to now, users solely rely on their knowledge and expertise when looking for explanatory theories. However, it often remains unclear whether the selected attribute ranges represent the real explanation for the feature of interest. Other selections hidden in the large number of data variables could potentially lead to similar features. Moreover, as simulation complexity grows, users are confronted with huge multidimensional data sets making it almost impossible to find meaningful hypotheses at all. We propose an interactive cycle of knowledge-based analysis and automatic hypothesis generation. Starting from initial hypotheses, created with linking and brushing, the user steers a heuristic search algorithm to look for alternative or related hypotheses. The results are analyzed in information visualization views that are linked to the volume rendering. Individual properties as well as global aggregates are visually presented to provide insight into the most relevant aspects of the generated hypotheses. This novel approach becomes computationally feasible due to a GPU implementation of the time-critical parts in the algorithm. A thorough evaluation of search times and noise sensitivity as well as a case study on data from the automotive domain substantiate the usefulness of the suggested approach.

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Index Terms:
Interactive Visual Analysis, Volumetric Data, Multiple Competing Hypotheses, Knowledge Discovery, Computer-assisted Multivariate Data Exploration, Curse of Dimensionality, Predictive Analysis, Genetic Algorithm
Citation:
Raphael Fuchs, Jürgen Waser, Meister Eduard Gröller, "Visual Human+Machine Learning," IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 6, pp. 1327-1334, Nov.-Dec. 2009, doi:10.1109/TVCG.2009.199
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