Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology Self-organizing information fusion and hierarchical knowledge discovery : a new framework using ARTMAP neural networks Presenter : Shu-Ya Li Authors : Gail A. Carpenter*, Siegfried Martens, Ogi J. Ogas © NN 2005

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Outline Motivation Objective Methodology Conclusion Personal Comments

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors working. Dog Puppy Animal Spot

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective Deriving consistent knowledge from inconsistent information. An ARTMAP neural network can act as a self-organizing expert system to derive hierarchical knowledge structures from inconsistent training data. Inconsistent training data ARTMAP Hierarchical knowledge

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Monterey  Ground truth pixels are labeled red car, other car, roof, road, foot path, grass, tree, other. Boston  Ground truth pixels are labeled ocean, ice, river, beach, park, road, residential, industrial, water, open space, built-up, natural, man-made. Example Red car and other car pixels are labeled vehicle. Road and foot path pixels are labeled pavement. Example The class natural includes water (ocean, ice, and river) The class Open space (includes beach and park) training testing validation If need

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology ARTMAP fusion system training protocol 75 pixels labeled road No pixels labeled ocean 4 pixels labeled road 19,919 pixels labeled ocean maximum number of labels (here set equal to 250) pixels ocean, ice, river, beach, park, road…

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Rules  equivalence parameter e = 90%  minimum confidence parameter c = 50% Remove C<c

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Graphical representations of knowledge hierarchies C < 50% Remove 50% < C < 90% C > 90%

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion The ARTMAP neural network produces one-to-many mappings from input vectors to output classes, as well as the more traditional many-to-one mappings. The procedure is not limited to the image domain.  drug resistance  improve marketing suggestions to individual consumers Deriving consistent knowledge from inconsistent information.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Personal Comments Advantage  discover hierarchical knowledge structures Drawback  … Application  drug resistance  improve marketing suggestions to individual consumers