Artificial Intelligence
Hierarchical censored production rules (HCPRs) system
Data & Knowledge Engineering
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Exploring the Power of Genetic Search in Learning Symbolic Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scalable Parallel Genetic Algorithms
Artificial Intelligence Review
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
Adaptive Hierarchical Censored Production Rule-Based System: A Generic Algorithm Approach
SBIA '96 Proceedings of the 13th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
A survey of evolutionary algorithms for data mining and knowledge discovery
Advances in evolutionary computing
Unified algorithm for undirected discovery of exception rules: Research Articles
International Journal of Intelligent Systems - Knowledge Discovery: Dedicated to Jan M. Żytkow
Search-intensive concept induction
Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Distributed mining of censored production rules in data streams: an evolutionary approach
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
Expert Systems with Applications: An International Journal
An evolutionary approach to discover intra-and inter-class exceptions in databases
International Journal of Intelligent Systems Technologies and Applications
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Exceptions, which focus on a very small portion of dataset, have been discarded as noise in machine learning. It is interesting to discover exceptions, as they challenge the existing knowledge and often lead to the growth of knowledge in new directions. Discovering exceptions from voluminous data sets still remains a great challenge. In Knowledge Discovery in Databases (KDD), it is also significant to extract the knowledge in a form that is flexible and efficient enough for approximate reasoning. A Censored Production Rule (CPR) is a special kind of knowledge structure that is represented as an augmented production rule of the form: If P Then D Unless C, where C (Censor) is an exception to rule. If P Then D part of CPR holds frequently while Unless C part holds rarely. Discovery of CPRs not only provides a computational mechanism to exhibit variable precision logic but also results in the discovery of a set of rules of considerably reduced size. In this paper, a Parallel Genetic Algorithm approach is suggested for automated discovery of Censored Production Rules. The parallel Genetic Algorithm involves both data and control parallelism. A fitness function that incorporates the constraints of CPRs, is proposed. The experimental results establish the effectiveness of the proposed algorithm.