Extracting linguistic rules from data sets using fuzzy logic and genetic algorithms

Authors:
Dan Meng;Zheng Pei
Affiliations:
School of Economics Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China;School of Mathematics & Computer Engineering, Xihua University, Chengdu 610039, China
Venue:
Neurocomputing
Year:
2012

Citing 38
Cited 1

Fuzzy connectives based crossover operators to model genetic algorithms population diversity

Fuzzy Sets and Systems
Three objective genetics-based machine learning for linguisitc rule extraction

Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems

Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
SAINTETIQ: a fuzzy set-based approach to database summarization

Fuzzy Sets and Systems - Data bases and approximate reasoning
Genetic learning of accurate and compact fuzzy rule based systems based on the 2-tuples linguistic representation

International Journal of Approximate Reasoning
Interpretability-preserving genetic optimization of linguistic terms in fuzzy models for fuzzy ordered classification: An ecological case study

International Journal of Approximate Reasoning
Letters: Training T-S norm neural networks to refine weights for fuzzy if-then rules

Neurocomputing
A fuzzified neural fuzzy inference network for handling both linguistic and numerical information simultaneously

Neurocomputing
A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing

Neurocomputing
A new approach to generate weighted fuzzy rules using genetic algorithms for estimating null values

Expert Systems with Applications: An International Journal
Continuous genetic algorithm-based fuzzy neural network for learning fuzzy IF-THEN rules

Neurocomputing
Extracting fuzzy rules based on fusion of soft computing in oil exploration management

Expert Systems with Applications: An International Journal
Design of interpretable fuzzy rule-based classifiers using spectral analysis with structure and parameters optimization

Fuzzy Sets and Systems
Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms

Fuzzy Sets and Systems
Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets

International Journal of Approximate Reasoning
Extracting complex linguistic data summaries from personnel database via simple linguistic aggregations

Information Sciences: an International Journal
Linguistic Values-based Intelligent Information Processing: Theory, Methods, and Applications

Linguistic Values-based Intelligent Information Processing: Theory, Methods, and Applications
A granular extension of the fuzzy-ARTMAP (FAM) neural classifier based on fuzzy lattice reasoning (FLR)

Neurocomputing
Non-uniform attractor embedding for time series forecasting by fuzzy inference systems

Neurocomputing
A fuzzy neural network with fuzzy impact grades

Neurocomputing
Genetic learning of fuzzy rules based on low quality data

Fuzzy Sets and Systems
A hybrid stock trading system for intelligent technical analysis-based equivolume charting

Neurocomputing
Modeling wine preferences by data mining from physicochemical properties

Decision Support Systems
GP-COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems

Information Sciences: an International Journal
A multiobjective evolutionary approach to concurrently learn rule and data bases of linguistic fuzzy-rule-based systems

IEEE Transactions on Fuzzy Systems
Fuzzy logic and evolutionary algorithm-two techniques in rule extraction from neural networks

Neurocomputing
Computing with words is an implementable paradigm: fuzzy queries, linguistic data summaries, and natural-language generation

IEEE Transactions on Fuzzy Systems - Special section on computing with words
On finity, countability, cardinalities, and cylindric extensions of type-2 fuzzy sets in linguistic summarization of databases

IEEE Transactions on Fuzzy Systems - Special section on computing with words
Solving multi-class problems with linguistic fuzzy rule based classification systems based on pairwise learning and preference relations

Fuzzy Sets and Systems
Rule extraction from support vector machines: A review

Neurocomputing
A probabilistic fuzzy approach to modeling nonlinear systems

Neurocomputing
Obtaining a complex linguistic data summaries from database based on a new linguistic aggregation operator

IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Interpreting and extracting fuzzy decision rules from fuzzy information systems and their inference

Information Sciences: an International Journal
Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy

IEEE Transactions on Evolutionary Computation
Design of adaptive fuzzy logic controller based on linguistic-hedgeconcepts and genetic algorithms

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy logic = computing with words

IEEE Transactions on Fuzzy Systems
Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base

IEEE Transactions on Fuzzy Systems
A Proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems and Its Interaction With Rule Selection

IEEE Transactions on Fuzzy Systems

Linguistic fuzzy model identification based on PSO with different length of particles

Applied Soft Computing

Quantified Score

Hi-index	0.01

Visualization

Abstract

Linguistic rules in natural language are useful and consistent with human way of thinking. They are very important in multi-criteria decision making due to their interpretability. In this paper, our discussions concentrate on extracting linguistic rules from data sets. In the end, we firstly analyze how to extract complex linguistic data summaries based on fuzzy logic. Then, we formalize linguistic rules based on complex linguistic data summaries, in which, the degree of confidence of linguistic rules from a data set can be explained by linguistic quantifiers and its linguistic truth from the fuzzy logical point of view. In order to obtain a linguistic rule with a higher degree of linguistic truth, a genetic algorithm is used to optimize the number and parameters of membership functions of linguistic values. Computational results show that the proposed method is an alternative method for extracting linguistic rules with linguistic truth from data sets.