In many applications, black-box prediction is not satisfactory, and understanding the data is
of critical importance. Typically, approaches useful for understanding of data involve logical
rules, evaluate similarity to prototypes, or are based on visualization or graphical methods.
This paper is focused on the extraction and use of logical rules for data understanding. All
aspects of rule generation, optimization, and application are described, including the
problem of finding good symbolic descriptors for continuous data, tradeoffs between …

The central challenge facing computational intelligence approaches is the problem of
extracting knowledge from data. How does one combine extracted knowledge with available
symbolic knowledge and refine the resulting knowledge-based expert systems? Black-box
statistical approaches may be good at deriving predictions from data, but formulating
understandable rules from the analysis of data is something entirely different from
formulating predictive models from that data. To build predictive data models, many data …