Elements of information theory
Elements of information theory
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Modified Classifier System Compaction Algorithm
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning
Fundamenta Informaticae
Mining breast cancer data with XCS
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Evolutionary rule-based systems for imbalanced data sets
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Intrusion detection with evolutionary learning classifier systems
Natural Computing: an international journal
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
LCSE: learning classifier system ensemble for incremental medical instances
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Data mining in learning classifier systems: comparing XCS with GAssist
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Evolutionary computing for knowledge discovery in medical diagnosis
Artificial Intelligence in Medicine
SLAVE: a genetic learning system based on an iterative approach
IEEE Transactions on Fuzzy Systems
Malware detection based on dependency graph using hybrid genetic algorithm
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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In this paper, we evaluate the performance of ten well-known evolutionary and non-evolutionary rule learning algorithms. The comparative study is performed on a real-world classification problem of detecting malicious executables. The executable dataset, used in this study, consists of 189 attributes which are statically extracted from the executables of Microsoft Windows operating system. In our study, we compare the performance of rule learning algorithms with respect to four metrics: (1) classification accuracy, (2) the number of rules in the developed rule set, (3) the comprehensibility of the generated rules, and (4) the processing overhead of the rule learning process. The results of our comparative study suggest that evolutionary rule learning classifiers cannot be deployed in real-world malware detection systems.