Data Fusion and Classification using a Hybrid Intrinsic Cellular Inference Network
Hybrid Intrinsic Cellular Inference Network (HICIN) is designed for battlespace decision support applications. We developed an automatic method of generating hypotheses for an entity-attribute classifier. The capability and effectiveness of a domain specific ontology was used to generate automatic categories for data classification. Heterogeneous data is clustered using an Adaptive Resonance Theory (ART) inference engine on a sample (unclassified) data set. The data set is the Lahman baseball database. The actual data is immaterial to the architecture, however, parallels in the data can be easily drawn (i.e., "Team" maps to organization, "Runs scored/allowed" to Measure of organization performance (positive/negative), "Payroll" to organization resources, etc.). Results show that HICIN classifiers create known inferences from the heterogonous data. These inferences are not explicitly stated in the ontological description of the domain and are strictly data driven. HICIN uses data uncertainty handling to reduce errors in the classification. The uncertainty handling is based on subjective logic. The belief mass allows evidence from multiple sources to be mathematically combined to increase or discount an assertion. In military operations the ability to reduce uncertainty will be vital in the data fusion operation.
R. S. Woodley et al., "Data Fusion and Classification using a Hybrid Intrinsic Cellular Inference Network," Proceedings of SPIE - The International Society for Optical Engineering, vol. 7710, SPIE, Apr 2010.
The definitive version is available at https://doi.org/10.1117/12.852666
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2010 (2010: Apr. 7-8, Orlando, FL)
Electrical and Computer Engineering
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