This paper introduces the Inverted Hierarchical
Neuro-Fuzzy BSP System (HNFB 1), a new neuro-fuzzy model
that has been specifically created for record classification and rule
extraction in databases. The HNFB 1 is based on the Hierarchical
Neuro-Fuzzy Binary Space Partitioning Model (HNFB), which
embodies a recursive partitioning of the input space, is able to
automatically generate its own structure, and allows a greater
number of inputs. The new HNFB 1 allows the extraction of
knowledge in the form of interpretable fuzzy rules expressed by
the following: If is and is , then input pattern belongs to
class . For the process of rule extraction in the HNFB 1 model,
two fuzzy evaluation measures were defined: 1) fuzzy accuracy and
2) fuzzy coverage. The HNFB 1 has been evaluated with different
benchmark databases for the classification task: Iris Dataset,Wine
Data, Pima Indians Diabetes Database, Bupa Liver Disorders,
and Heart Disease. When compared with several other pattern
classification models and algorithms, the HNFB 1 model has
shown similar or better classification performance. Nevertheless,
its performance in terms of processing time is remarkable. The
HNFB 1 converged in less than one minute for all the databases
described in the case study.
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