@misc{10481/64939,
year = {2015},
url = {http://hdl.handle.net/10481/64939},
abstract = {The current financial crisis has
stressed the need of obtaining more accurate
prediction models in order to decrease the risk when
investing money on economic opportunities. In
addition, the transparency of the process followed to
make the decisions in financial applications is
becoming an important issue. Furthermore, there is a
need to handle the real-world imbalanced financial
data sets without using sampling techniques which
might introduce noise in the used data. In this paper,
we present a compact evolutionary interval-valued
fuzzy rule-based classification system, which is
based on IVTURSFARC-HD (Interval-Valued fuzzy rulebased classification system with TUning and Rule
Selection) [22]), for the modeling and prediction of
real-world financial applications. This proposed
system allows obtaining good predictions accuracies
using a small set of short fuzzy rules implying a high
degree of interpretability of the generated linguistic
model. Furthermore, the proposed system deals with
the financial imbalanced datasets with no need for
any preprocessing or sampling method and thus
avoiding the accidental introduction of noise in the
data used in the learning process. The system is also
provided with a mechanism to handle examples that
are not covered by any fuzzy rule in the generated
rule base. To test the quality of our proposal, we will
present an experimental study including eleven realworld financial datasets. We will show that the
proposed system outperforms the original C4.5
decision tree, type-1 and interval-valued fuzzy
counterparts which use the SMOTE sampling
technique to preprocess data and the original FURIA,
which is a fuzzy approximative classifier.
Furthermore, the proposed method enhances the
results achieved by the cost sensitive C4.5 and it
gives competitive results when compared with
FURIA using SMOTE, while our proposal avoids
pre-processing techniques and it provides
interpretable models that allow obtaining more
accurate results.},
organization = {Spanish Government

TIN2011-28488
TIN2013-40765-P},
publisher = {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC},
keywords = {Financial applications},
keywords = {IntervalValued Fuzzy Sets},
keywords = {Interval-Valued Fuzzy RuleBased Classification Systems},
keywords = {Evolutionary algorithms},
title = {A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications with Imbalanced Data},
doi = {10.1109/TFUZZ.2014.2336263},
author = {Sanz, José Antonio and Herrera Triguero, Francisco and Bustince, Humberto},
}