C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: concepts and techniques
Data mining: concepts and techniques
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
An Imunogenetic Technique To Detect Anomalies In Network Traffic
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms
IEEE Transactions on Fuzzy Systems
Accelerating improvement of fuzzy rules induction with artificial immune systems
WSEAS TRANSACTIONS on SYSTEMS
Accelerating improvement of fuzzy rules induction with artificial immune systems
WSEAS TRANSACTIONS on SYSTEMS
Speed boosting induction of fuzzy rules with artificial immune systems
ICS'08 Proceedings of the 12th WSEAS international conference on Systems
Mining fuzzy rules using an Artificial Immune System with fuzzy partition learning
Applied Soft Computing
Diagnosis of cardiac arrhythmia using fuzzy immune approach
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Machine learning approach to model sport training
Computers in Human Behavior
A novel chemistry based metaheuristic optimization method for mining of classification rules
Expert Systems with Applications: An International Journal
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In this study, a classification model including fuzzy system, artificial immune system (AIS), and boosting is proposed. The model is mainly focused on the clonal selection principle of biological immune system and evolves a population of antibodies, where each antibody represents the antecedent of a fuzzy classification rule while each antigen represents an instance. The fuzzy classification rules are mined in an incremental fashion, in that the AIS optimizes one rule at a time. The boosting mechanism that is used to increase the accuracy rates of the rules reduces the weight of training instances that are correctly classified by the new rule. Whenever AIS mines a rule, this rule is added to the mined rule list and mining of next rule focuses on rules that account for the currently uncovered or misclassified instances. The results obtained by proposed approach are analyzed with respect to predictive accuracy and simplicity and compared with C4.5Rules.