Competitive exception learning using fuzzy frequency distributions
A competitive exception learning algorithm for finding a non-linear mapping is proposed which puts the emphasis on the discovery of the important exceptions rather than the main rules. To do so,we first cluster the output space using a competitive fuzzy clustering algorithm and derive a fuzzy frequency distribution describing the general, average system's output behavior. Next, we look for a fuzzy partitioning of the input space in such away that the corresponding fuzzy output frequency distributions `deviate at most' from the average one as found in the first step. In this way, the most important `exceptional regions' in the input-output relation are determined. Using the joint input-output fuzzy frequency distributions, the complete input-output function as extracted from the data, can be expressed mathematically. In addition, the exceptions encountered can be collected and described as a set of fuzzy if-then-else-rules. Besides presenting a theoretical description of the new exception learning algorithm, we report on the outcomes of certain practical simulations.
|Keywords||competitive learning, exception learning, fuzzy pattern recognition|
|Publisher||Erasmus Research Institute of Management (ERIM)|
van den Bergh, W.M., & van den Berg, J.. (2000). Competitive exception learning using fuzzy frequency distributions (No. ERS-2000-06-LIS). Erasmus Research Institute of Management (ERIM). Retrieved from http://hdl.handle.net/1765/15