Document Detail


Visual human+machine learning.
MedLine Citation:
PMID:  19834205     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Raphael Fuchs; J??rgen Waser; Meister Eduard Gr??ller
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  IEEE transactions on visualization and computer graphics     Volume:  15     ISSN:  1077-2626     ISO Abbreviation:  IEEE Trans Vis Comput Graph     Publication Date:    2009 Nov-Dec
Date Detail:
Created Date:  2009-10-16     Completed Date:  2010-01-13     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9891704     Medline TA:  IEEE Trans Vis Comput Graph     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1327-34     Citation Subset:  -    
Affiliation:
ETH Zurich. raphael@inf.ethz.ch
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