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Título:NEURAL-FUZZY HIERARCHICAL MODELS FOR PATTERN CLASSIFICATION AND FUZZY RULE EXTRACTION FROM DATABASES Instituição:PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO - PUC-RIO Autor(es):LAERCIO BRITO GONCALVES
This dissertation investigates the use of Neuro-Fuzzy
Hierarchical BSP (Binary Space
Partitioning) systems for pattern classification and
extraction of fuzzy rules in databases. The
objective of this work was to create specific models for
the classification of registers based on
the Neuro-Fuzzy BSP model that is able to create its
structure automatically and to extract
linguistic rules that explain the data structure. The task
of pattern classification is to find
relationships between data with the intention of
forecasting the class of an unknown pattern.
The work consisted of four parts: study about the main
methods of the pattern
classification; evaluation of the original Neuro-Fuzzy
Hierarchical BSP system (NFHB) in
pattern classification; definition and implementation of
two NFHB systems dedicated to
pattern classification; and case studies.
The study about classification methods resulted in a survey
on the area, where the
main techniques used for pattern classification are
described. The main techniques are:
statistic methods, genetic algorithms, decision trees,
neural networks, and neuro-fuzzy
The evaluation of the NFHB system in pattern classification
took in to consideration
the particularities of the model which has: ability to
create its own structure; recursive space
partitioning; ability to deal with more inputs than other
neuro-fuzzy system; and recursive
fuzzy rules. The original NFHB system, however, is unsuited
for pattern classification. The
original NFHB model has only one output and its use in
classification problems makes it
necessary to create a criterion of band value (windows) in
order to represent the classes.
Therefore, it was decided to create new models that could
overcome this deficiency.
Two new NFHB systems were developed for pattern
and NFHB-Class. The first one creates its structure using
the same learning algorithm of the
original NFHB system. After the structure has been created,
it is inverted (see chapter 5) for
the generalization process. The inversion of the structure
provides the system with the number
of outputs equal to the number of classes in the database.
The second system, the NFHB-Class
uses an inverted version of the original basic NFHB cell in
both phases, learning and
validation. Both systems proposed have the number of
outputs equal to the number of the
pattern classes, what means a great differential in
relation to the original NFHB model.
Besides the pattern classification objective, the NFHB-
Class system was able to extract
knowledge in form of interpretable fuzzy rules. These rules
are expressed by this way: If x is
A and y is B then the pattern belongs to Z class.
The two models developed have been tested in many case
Benchmark databases for classification task, such as: Iris
Dataset, Wine Data, Pima Indians
Diabetes Database, Bupa Liver Disorders and Heart Disease,
where comparison has been
made with several traditional models and algorithms of
The results found with NFHB-Invertido and NFHB-Class
models, in all cases, showed
to be superior or equal to the best results found by the
others models and algorithms for
pattern classification. The performance of the NFHB-
Invertido and NFHB-Class models in
terms of time-processing were also very good. For all
databases described in the case studies
(chapter 8), the models converged to an optimal
classification solution, besides the fuzzy rules
extraction, in a time-processing inferior to a minute.