Tesis y Tesistas 2020 - Postgrado - Fac. de Informática - UNLP

DOCTORADO EN
CIENCIAS INFORMÁTICAS
Dr. Patricia Rosalia
Jimbo Santana
e-mail
prjimbo@uce.edu.ec
Advisors
Dr. Laura Lanzarini
Dr. Aurelio F Fernández Bariviera
Thesis defense date
July 14, 2020
SEDICI
http://sedici.unlp.edu.ar/handle/10915/101163
Obtaining fuzzy classification rules
using optimization techniques.
Credit Risk case study
Keywords: Fuzzy Classification Rules, Variable Particle Swarm Optimization, Data Mining.
Motivation
In recent years thanks to the advancement of technology,
organizations have stored a lot of information. This has led
them to the need of incorporating techniques to process
and obtain information from useful data.
Within the KDD (Knowledge Discovery in Databases), Data
Mining is considered the most important phase, as it
groups together the techniques capable of modeling the
information. From the use or understanding of the model
it is possible extract knowledge. This generated knowledge
results of great interest to organizations, since it constitutes
an extremely important tool for tactical and strategic
decisions, which becomes a competitive advantage.
A desirable feature of the models is to extract knowledge
in understandable terms. In this sense, classification
rules are considered by decision makers, as one of the
most understandable forms that can be used to represent
knowledge, since they have the ability to explain themselves.
If you add to this that classification rules use fuzzy logic
through fuzzy sets to describe the values o even easier,
bringing us ever closer to human reasoning.
The main objective of this thesis is to contribute to data
mining with a new method to obtain fuzzy classification
rules, and to the area of financial risk through the evaluation
of the rules.
Thesis contributions
The central contribution of this thesis is the definition of a new
method able of generating a set of fuzzy classification rules of
easy interpretation, low cardinality and good accuracy. These
features help to identify and understand the relationships
presented in data, facilitating decision making.
The new proposed method is called FRvarPSO (Fuzzy Rules
variable Particle Swarm Oprmization). It combines a
competitive neural network with an optimization technique
based on variable population particles clusters to obtain
fuzzy classification rules. It is capable of working with
nominal and numerical attributes. The antecedents of the
rules are made up of nominal attributes and / or fuzzy
conditions. The conformation of the latter requires knowing
the membership degree to the fuzzy sets of each linguistic
variable. This thesis proposes three different alternatives to
solve this point.
Regarding to obtaining the rules, the proposed method
uses an iterative process where the examples of a class are
covered until the desired coverage is achieved. Therefore, the
rule’s consequent is determined by the selected class and the
antecedent is extracted through the optimization technique.
One of the contributions of this thesis lies in the definition
of the fitness function of each particle based on a ”Voting
criterion”, that weights the participation of the fuzzy
conditions in the formation of the antecedent. Its value is
obtained from the degrees of membership of the examples
that abide by the rule and is used to reinforce the movement
of the particle in the direction where the highest value is
located. With the use of PSO the particles compete among
them to find the best rule of the selected class.
The efficiency and efficacy of FRvarPSO are strongly driven
by the way fuzzy sets membership functions are determined.
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