Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm
Pattern Recognition Letters
Learning fuzzy classification rules from labeled data
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
Particle swarm based Data Mining Algorithms for classification tasks
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
Finding useful fuzzy concepts for pattern classification using genetic algorithm
Information Sciences: an International Journal
A weighting function for improving fuzzy classification systems performance
Fuzzy Sets and Systems
Fuzzy classifier design using genetic algorithms
Pattern Recognition
Classification tree analysis using TARGET
Computational Statistics & Data Analysis
Designing of classifiers based on immune principles and fuzzy rules
Information Sciences: an International Journal
Construction of classifier ensembles by means of artificial immune systems
Journal of Heuristics
A memetic algorithm for evolutionary prototype selection: A scaling up approach
Pattern Recognition
A multilayered neuro-fuzzy classifier with self-organizing properties
Fuzzy Sets and Systems
A Hybrid Optimization Method for Fuzzy Classification Systems
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Rule extraction from trained adaptive neural networks using artificial immune systems
Expert Systems with Applications: An International Journal
A hybrid coevolutionary algorithm for designing fuzzy classifiers
Information Sciences: an International Journal
Performance and efficiency of memetic pittsburgh learning classifier systems
Evolutionary Computation
An evolutionary memetic algorithm for rule extraction
Expert Systems with Applications: An International Journal
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A dynamic artificial immune algorithm applied to challenging benchmarking problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
IEEE Transactions on Evolutionary Computation
Fuzzy Classifier Design
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Information Sciences: an International Journal
Design of fuzzy rule-based classifiers with semantic cointension
Information Sciences: an International Journal
Supervised Pseudo Self-Evolving Cerebellar algorithm for generating fuzzy membership functions
Expert Systems with Applications: An International Journal
A diversity-driven structure learning algorithm for building hierarchical neuro-fuzzy classifiers
Information Sciences: an International Journal
Designing ensembles of fuzzy classification systems: an immune-inspired approach
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Inducing oblique decision trees with evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary learning of hierarchical decision rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary design of a fuzzy classifier from data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An incremental approach to genetic-algorithms-based classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy classifier with ellipsoidal regions
IEEE Transactions on Fuzzy Systems
SLAVE: a genetic learning system based on an iterative approach
IEEE Transactions on Fuzzy Systems
Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms
IEEE Transactions on Fuzzy Systems
SGERD: A Steady-State Genetic Algorithm for Extracting Fuzzy Classification Rules From Data
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
Information Sciences: an International Journal
A genetic design of linguistic terms for fuzzy rule based classifiers
International Journal of Approximate Reasoning
Extraction of fuzzy rules from fuzzy decision trees: An axiomatic fuzzy sets (AFS) approach
Data & Knowledge Engineering
Adaptability, interpretability and rule weights in fuzzy rule-based systems
Information Sciences: an International Journal
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In this study, we propose a novel approach for designing fuzzy classifiers. The first part of our approach is a new preprocess algorithm called SPP (silhouette cluster validity index aided pre-process via k-means). The SPP algorithm has been performed on the data set to determine the numbers of the membership functions and their initial boundaries. Then, the Mopt-aiNetLS algorithm (modified version of opt-aiNet combined with local search strategy of memetic algorithm), the second part of the approach, examines search space to find the optimal values of fuzzy rules and membership functions for the system. The Mopt-aiNetLS is the combination of the memetic algorithm and a modified version of the opt-aiNet algorithm, in which some changes were made in the suppression and hypermutation mechanisms of the original opt-aiNet algorithm. These two new mechanisms are called the intelligent suppression mechanism and the adaptive hypermutation operator. Combining the modified version of opt-aiNet with the local search strategy of the memetic algorithm improves the accuracy of the classification rate. An effective search process has been realized using the Mopt-aiNetLS because the global search capability of opt-aiNet is complemented by the local search strategy of the memetic algorithm. To test the performance of this new approach, twenty different well-known classification dataset benchmark problems from the UCI dataset were used. The average 3x10 cross-fold validation results obtained from these datasets are presented and compared with the results of certain classification algorithms reported in the literature. The Wilcoxon Signed-Rank Test was also used for statistical comparisons. The obtained results demonstrate the effectiveness of the proposed approach.