Fusing multiple data and knowledge sources for signal understanding by genetic algorithm

This paper presents a new approach to partially automating a human expert's proficient interpretation skills for data and knowledge fusion in signal-understanding tasks. The authors start by recognizing the fact that signal interpretation is attributed much to a human expert's domain-specific, pattern-perceiving capability of grasping raw signals by structured representations having multiple levels of abstraction, rather than to some objectively defined knowledge. In other words, that is an emergent or self-organizing process, where information is regarded as perceptual as opposed to objectively defined. First, they attempt to organize such structured representations by usage of a hierarchical clustering method of data analysis. Then, based on these representations they model a human expert's interpretation skill as an activity of searching for an optimum combination of those perceptual units within that structured representation space being constrained by the data. In order to implement this activity, they introduce a genetic algorithm and apply it to the structured representation space assimilating a human analyst's creative interpreting task in flexibly shifting the focal view of attention from the coarse to the precise. They implement a working system for signal understanding of the remote sensing data of seismic prospecting and show the results output by the system.