C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Classification Algorithm for Multi-Echo Magnetic Resonance Image Using Gibbs Distributions
ICSC '95 Proceedings of the Third International Computer Science Conference on Image Analysis Applications and Computer Graphics
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Feature Selection with Decision Tree Criterion
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Frequent sub-graph mining on edge weighted graphs
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Graph aggregation based image modeling and indexing for video annotation
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Frequent approximate subgraphs as features for graph-based image classification
Knowledge-Based Systems
Data mining techniques for the screening of age-related macular degeneration
Knowledge-Based Systems
Time series case based reasoning for image categorisation
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
A new proposal for graph classification using frequent geometric subgraphs
Data & Knowledge Engineering
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An approach to classifying magnetic resonance (MR) image data is described. The specific application is the classification of MRI scan data according to the nature of the corpus callosum, however the approach has more general applicability. A variation of the ''spectral segmentation with multi-scale graph decomposition'' mechanism is introduced. The result of the segmentation is stored in a quad-tree data structure to which a weighted variation (also developed by the authors) of the gSpan algorithm is applied to identify frequent sub-trees. As a result the images are expressed as a set frequent sub-trees. There may be a great many of these and thus a decision tree based feature reduction technique is applied before classification takes place. The results show that the proposed approach performs both efficiently and effectively, obtaining a classification accuracy of over 95% in the case of the given application.