730 research outputs found
An Analysis of the Rule Weights and Fuzzy Reasoning Methods for Linguistic Rule Based Classification Systems Applied to Problems with Highly Imbalanced Data Sets
In this contribution we carry out an analysis of the rule
weights and Fuzzy Reasoning Methods for Fuzzy Rule Based Classification
Systems in the framework of imbalanced data-sets with a high
imbalance degree. We analyze the behaviour of the Fuzzy Rule Based
Classification Systems searching for the best configuration of rule weight
and Fuzzy Reasoning Method also studying the cooperation of some
pre-processing methods of instances. To do so we use a simple rule base
obtained with the Chi (and co-authors’) method that extends the wellknown
Wang and Mendel method to classification problems.
The results obtained show the necessity to apply an instance preprocessing
step and the clear differences in the use of the rule weight
and Fuzzy Reasoning Method.
Finally, it is empirically proved that there is a superior performance
of Fuzzy Rule Based Classification Systems compared to the 1-NN and
C4.5 classifiers in the framework of highly imbalanced data-sets.Spanish Projects TIN-2005-08386-C05-01 & TIC-2005-08386-
C05-0
Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets
In many real application areas, the data used are highly skewed and the number of
instances for some classes are much higher than that of the other classes. Solving a classification
task using such an imbalanced data-set is difficult due to the bias of the training
towards the majority classes.
The aim of this paper is to improve the performance of fuzzy rule based classification systems
on imbalanced domains, increasing the granularity of the fuzzy partitions on the
boundary areas between the classes, in order to obtain a better separability. We propose
the use of a hierarchical fuzzy rule based classification system, which is based on the
refinement of a simple linguistic fuzzy model by means of the extension of the structure
of the knowledge base in a hierarchical way and the use of a genetic rule selection process
in order to get a compact and accurate model.
The good performance of this approach is shown through an extensive experimental
study carried out over a large collection of imbalanced data-sets.Spanish Ministry of Education and Science (MEC) under Projects TIN-2005-08386-C05-01 and TIN-2005-08386-
C05-0
A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data
The current financial crisis has stressed the need to obtain more accurate prediction models in order to decrease 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 real-world imbalanced financial datasets without using sampling techniques that 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 interval-valued fuzzy rule-based classification system with tuning and rule selection (IVTURS FA RC-HD ) for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good prediction 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 11 real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1, and interval-valued fuzzy counterparts that use the synthetic minority oversampling technique (SMOTE) 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 preprocessing techniques, and it provides interpretable models that allow obtaining more accurate results
A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications with Imbalanced Data
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.Spanish Government
TIN2011-28488
TIN2013-40765-
On the role of pre and post-processing in environmental data mining
The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed
Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective
Studies in Evolutionary Fuzzy Systems (EFSs) began in the 90s and have experienced a fast development since then, with applications to areas such as pattern recognition, curve‐fitting and regression, forecasting and control. An EFS results from the combination of a Fuzzy Inference System (FIS) with an Evolutionary Algorithm (EA). This relationship can be established for multiple purposes: fine‐tuning of FIS's parameters, selection of fuzzy rules, learning a rule base or membership functions from scratch, and so forth. Each facet of this relationship creates a strand in the literature, as membership function fine‐tuning, fuzzy rule‐based learning, and so forth and the purpose here is to outline some of what has been done in each aspect. Special focus is given to Genetic Programming‐based EFSs by providing a taxonomy of the main architectures available, as well as by pointing out the gaps that still prevail in the literature. The concluding remarks address some further topics of current research and trends, such as interpretability analysis, multiobjective optimization, and synthesis of a FIS through Evolving methods
Literature Review of the Recent Trends and Applications in various Fuzzy Rule based systems
Fuzzy rule based systems (FRBSs) is a rule-based system which uses linguistic
fuzzy variables as antecedents and consequent to represent human understandable
knowledge. They have been applied to various applications and areas throughout
the soft computing literature. However, FRBSs suffers from many drawbacks such
as uncertainty representation, high number of rules, interpretability loss,
high computational time for learning etc. To overcome these issues with FRBSs,
there exists many extensions of FRBSs. This paper presents an overview and
literature review of recent trends on various types and prominent areas of
fuzzy systems (FRBSs) namely genetic fuzzy system (GFS), hierarchical fuzzy
system (HFS), neuro fuzzy system (NFS), evolving fuzzy system (eFS), FRBSs for
big data, FRBSs for imbalanced data, interpretability in FRBSs and FRBSs which
use cluster centroids as fuzzy rules. The review is for years 2010-2021. This
paper also highlights important contributions, publication statistics and
current trends in the field. The paper also addresses several open research
areas which need further attention from the FRBSs research community.Comment: 49 pages, Accepted for publication in ijf
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