C4.5: programs for machine learning
C4.5: programs for machine learning
BOAT—optimistic decision tree construction
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Understanding the Crucial Role of AttributeInteraction in Data Mining
Artificial Intelligence Review
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
An Experimental Evaluation of Coevolutive Concept Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Discovery of Decision Rules from Databases: An Evolutionary Approach
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
RainForest - A Framework for Fast Decision Tree Construction of Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Design and Implementation of a Genetic-Based Algorithm for Data Mining
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
An iterative method for multi-class cost-sensitive learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Video Data Mining: Semantic Indexing and Event Detection from the Association Perspective
IEEE Transactions on Knowledge and Data Engineering
Architecture for an Artificial Immune System
Evolutionary Computation
Test-Cost Sensitive Classification on Data with Missing Values
IEEE Transactions on Knowledge and Data Engineering
A Fuzzy Approach to Partitioning Continuous Attributes for Classification
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Some Effective Techniques for Naive Bayes Text Classification
IEEE Transactions on Knowledge and Data Engineering
Classifier Ensembles with a Random Linear Oracle
IEEE Transactions on Knowledge and Data Engineering
Anonymizing Classification Data for Privacy Preservation
IEEE Transactions on Knowledge and Data Engineering
Toward Exploratory Test-Instance-Centered Diagnosis in High-Dimensional Classification
IEEE Transactions on Knowledge and Data Engineering
Classifier fitness based on accuracy
Evolutionary Computation
Search-intensive concept induction
Evolutionary Computation
Flexible learning of problem solving heuristics through adaptive search
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Intrusion detection based on clustering organizational co-evolutionary classification
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
NOW G-Net: learning classification programs on networks ofworkstations
IEEE Transactions on Evolutionary Computation
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
An organizational coevolutionary algorithm for classification
IEEE Transactions on Evolutionary Computation
A multiagent genetic algorithm for global numerical optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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By inspiration of the granular evolutionary algorithm, a Granular Agent Evolutionary Classification (GAEC) algorithm for the classification task in data mining is proposed. The method uses the granular agent to denote the set of some examples that have similar attributions and the knowledge base guides the evolution of granular agent. Also some granular evolutionary operators are designed for classification problem. Assimilation operator, exchange operator, and differentiation operator reflect the competitive, cooperative and self-learning ability of agent, respectively. Finally, some classification rules are extracted from granular agents by some strategy to forecast the sort of new data. Empirical study contains UCI data sets, KDDCUP99 data sets and remote image recognition. The test results show that the algorithm has a good classification prediction, and only need a small price for the training time. In most UCI data sets, the performance of GAEC is better than G-NET, OCEC and C4.5, which have good performance. At the same time, some Gaussian White Noise attributes are added to these UCI data sets and the results show GACE has some anti-noise abilities. To test the scalability of GAEC, two functions along two dimensions, the number of training examples and the number of attributes are used. Also, GAEC are applied to some real world fields, intrusion detection system and remote sensing image recognition. The experiments for KDDCUP99 verify GAEC has capability to deal with massive data in real world and good predicting capability for unknown type data. At last, the accuracy rate of GAEC is also good for the remote sensing image recognition.