Using gene expression programming to construct sentence ranking functions for text summarization
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
A distributed evolutionary method to design scheduling policies for volunteer computing
Proceedings of the 5th conference on Computing frontiers
Using differential evolution for symbolic regression and numerical constant creation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
CACS: A Novel Classification Algorithm Based on Concept Similarity
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
GEP-Induced Expression Trees as Weak Classifiers
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Indirect Online Evolution --- A Conceptual Framework for Adaptation in Industrial Robotic Systems
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
A Genetic Programming Classifier Design Approach for Cell Images
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A distributed evolutionary method to design scheduling policies for volunteer computing
ACM SIGMETRICS Performance Evaluation Review
Gene Expression Programming Neural Network for Regression and Classification
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
An Improved Gene Expression Programming for Fuzzy Classification
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Web Application Security through Gene Expression Programming
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
On the evolution of neural networks for pairwise classification using gene expression programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Multi-label Classification with Gene Expression Programming
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Mining Class Contrast Functions by Gene Expression Programming
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Self-emergence of structures in gene expression programming
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Dynamic split-point selection method for decision tree evolved by gene expression programming
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Detecting web application attacks with use of gene expression programming
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Unconstrained gene expression programming
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Coevolving heuristics for the distributor's pallet packing problem
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A Family of GEP-Induced Ensemble Classifiers
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
A niching algorithm to learn discriminant functions with multi-label patterns
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Gene expression programming for induction of finite transducer
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Two ensemble classifiers constructed from GEP-induced expression trees
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
Mining contrast inequalities in numeric dataset
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Soft computing techniques for intrusion detection of SQL-based attacks
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
IEEE Transactions on Evolutionary Computation
Cellular GEP-induced classifiers
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartI
An iterative artificial neural network for high dimensional data analysis
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Cellular gene expression programming classifier learning
Transactions on computational collective intelligence V
Distance guided classification with gene expression programming
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
VCCM mining: mining virtual community core members based on gene expression programming
WISI'06 Proceedings of the 2006 international conference on Intelligence and Security Informatics
Using gene expression programming to infer gene regulatory networks from time-series data
Computational Biology and Chemistry
The trading on the mutual funds by gene expression programming with Sortino ratio
Applied Soft Computing
Solving symbolic regression problems with uniform design-aided gene expression programming
The Journal of Supercomputing
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Classification is one of the fundamental tasks of data mining. Most rule induction and decision tree algorithms perform a local, greedy search to generate classification rules that are often more complex than necessary. Evolutionary algorithms for pattern classification have recently received increased attention because they can perform global searches. In this paper, we propose a new approach for discovering classification rules by using gene expression programming (GEP), a new technique of genetic programming (GP) with linear representation. The antecedent of discovered rules may involve many different combinations of attributes. To guide the search process, we suggest a fitness function considering both the rule consistency gain and completeness. A multiclass classification problem is formulated as multiple two-class problems by using the one-against-all learning method. The covering strategy is applied to learn multiple rules if applicable for each class. Compact rule sets are subsequently evolved using a two-phase pruning method based on the minimum description length (MDL) principle and the integration theory. Our approach is also noise tolerant and able to deal with both numeric and nominal attributes. Experiments with several benchmark data sets have shown up to 20% improvement in validation accuracy, compared with C4.5 algorithms. Furthermore, the proposed GEP approach is more efficient and tends to generate shorter solutions compared with canonical tree-based GP classifiers.