Distributed representation of fuzzy rules and its application to pattern classification
Fuzzy Sets and Systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
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A Simple Approach to Ordinal Classification
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Multiclass Alternating Decision Trees
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Machine Learning
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Fuzzy integral-based perceptron for two-class pattern classification problems
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Gene expression profile class prediction using linear Bayesian classifiers
Computers in Biology and Medicine
The impact of parametrization in memetic evolutionary algorithms
Theoretical Computer Science
An interpretable fuzzy rule-based classification methodology for medical diagnosis
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A Hybrid Feature Selection Method Using Gene Expression Data
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Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Exploiting scale-free information from expression data for cancer classification
Computational Biology and Chemistry
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Theoretical Aspects of Local Search
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Estimating continuous distributions in Bayesian classifiers
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PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Speeding up logistic model tree induction
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Evolutionary computing for knowledge discovery in medical diagnosis
Artificial Intelligence in Medicine
Self-adaptive neuro-fuzzy inference systems for classification applications
IEEE Transactions on Fuzzy Systems
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Cancer is one of the key research topics in the medical field. An accurate detection of different cancer tumor types has great value in providing better treatment facilities and risk minimization for patients. Recently, DNA microarray-based gene expression profiles have been employed to correlate the clinical behavior of cancers with the differential gene expression levels in cancerous and benign tumors. An accurate classifier with linguistic interpretability using a small number of relevant genes is beneficial to microarray data analysis and development of inexpensive diagnostic tests. Several well-known and frequently used techniques for designing classifiers from microarray data, such as a support vector machine, neural networks, k-nearest neighbor, and logistic regression model, suffer from low comprehensibility. This paper proposes a new memetic algorithm which is capable of extracting interpretable and accurate fuzzy if-then rules from cancer data. This paper is the first proposal of memetic algorithms with the Multi-View fitness function approach. The new presented Multi-View fitness function considers two kinds of evaluating procedures. The first procedure, which is located in the main evolutionary structure of the algorithm, evaluates each single fuzzy if-then rule according to the specified rule quality (the evaluating procedure does not consider other rules). However, the second procedure determines the quality of each fuzzy rule according to the whole fuzzy rule set performance. In comparison to classic memetic algorithms, these kinds of memetic algorithms enhance the rule discovery process significantly.