Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Fuzzy integral-based perceptron for two-class pattern classification problems
Information Sciences: an International Journal
Artificial neural network model for voltage security based contingency ranking
Applied Soft Computing
Two-stage classification methods for microarray data
Expert Systems with Applications: An International Journal
Generating fuzzy rules from training instances for fuzzy classification systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An expert system to classify microarray gene expression data using gene selection by decision tree
Expert Systems with Applications: An International Journal
A sequential feature extraction approach for naïve bayes classification of microarray data
Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Data mining of gene expression data by fuzzy and hybrid fuzzy methods
IEEE Transactions on Information Technology in Biomedicine
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Knowledge gained through classification of microarray gene expression data is increasingly important as they are useful for phenotype classification of diseases. Different from black box methods, fuzzy expert system can produce interpretable classifier with knowledge expressed in terms of if-then rules and membership function. This paper proposes a novel Genetic Swarm Algorithm (GSA) for obtaining near optimal rule set and membership function tuning. Advanced and problem specific genetic operators are proposed to improve the convergence of GSA and classification accuracy. The performance of the proposed approach is evaluated using six gene expression data sets. From the simulation study it is found that the proposed approach generated a compact fuzzy system with high classification accuracy for all the data sets when compared with other approaches.