Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Rough set algorithms in classification problem
Rough set methods and applications
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Learning fuzzy classification rules from labeled data
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
AI Communications - Special issue on Artificial intelligence advances in China
Supervised fuzzy clustering for the identification of fuzzy classifiers
Pattern Recognition Letters
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ICCI '04 Proceedings of the Third IEEE International Conference on Cognitive Informatics
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
International Journal of Approximate Reasoning
Decision tree search methods in fuzzy modeling and classification
International Journal of Approximate Reasoning
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
A hybrid approach for feature subset selection using neural networks and ant colony optimization
Expert Systems with Applications: An International Journal
Letting ants labeling point features [sic.: for 'labeling' read 'label']
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Fuzzy classifier identification using decision tree and multiobjective evolutionary algorithms
International Journal of Approximate Reasoning
Rescheduling and optimization of logistic processes using GA and ACO
Engineering Applications of Artificial Intelligence
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Fuzzy-rough data reduction with ant colony optimization
Fuzzy Sets and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
An agent-based framework for distributed learning
Engineering Applications of Artificial Intelligence
Comparison of different input selection algorithms in neuro-fuzzy modeling
Expert Systems with Applications: An International Journal
Fuzzy criteria for feature selection
Fuzzy Sets and Systems
Efficient ant colony optimization for image feature selection
Signal Processing
| Hi-index | 12.05 |
The available set of potential features in real-world databases is sometimes very large, and it can be necessary to find a small subset for classification purposes. One of the most important techniques in data pre-processing for classification is feature selection. Less relevant or highly correlated features decrease, in general, the classification accuracy and enlarge the complexity of the classifier. The goal is to find a reduced set of features that reveals the best classification accuracy for a classifier. Rule-based fuzzy models can be acquired from numerical data, and be used as classifiers. As rule based structures revealed to be a useful qualitative description for classification systems, this work uses fuzzy models as classifiers. This paper proposes an algorithm for feature selection based on two cooperative ant colonies, which minimizes two objectives: the number of features and the classification error. Two pheromone matrices and two different heuristics are used for these objectives. The performance of the method is compared with other features selection methods, achieving equal or better performance.