A Knowledge-Intensive Genetic Algorithm for Supervised Learning
Machine Learning - Special issue on genetic algorithms
Learning in the presence of concept drift and hidden contexts
Machine Learning
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Parallel Genetic Algorithms
Artificial Intelligence Review
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
ECML '93 Proceedings of the European 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
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Automated alphabet reduction method with evolutionary algorithms for protein structure prediction
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Using multiple windows to track concept drift
Intelligent Data Analysis
Classifier fitness based on accuracy
Evolutionary Computation
A Low-Granularity Classifier for Data Streams with Concept Drifts and Biased Class Distribution
IEEE Transactions on Knowledge and Data Engineering
Classifying Data Streams with Skewed Class Distributions and Concept Drifts
IEEE Internet Computing
A Genetic Algorithm-Based Approach for Classification Rule Discovery
ICIII '08 Proceedings of the 2008 International Conference on Information Management, Innovation Management and Industrial Engineering - Volume 01
Adapted One-versus-All Decision Trees for Data Stream Classification
IEEE Transactions on Knowledge and Data Engineering
Learning concept classification rules using genetic algorithms
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Scaling Genetic Algorithms Using MapReduce
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Evolutionary learning of hierarchical decision rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An incremental approach to genetic-algorithms-based classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A similarity-based approach for data stream classification
Expert Systems with Applications: An International Journal
Multi-objective PSO algorithm for mining numerical association rules without a priori discretization
Expert Systems with Applications: An International Journal
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Recent research shows that rule based models perform well while classifying large data sets such as data streams with concept drifts. A genetic algorithm is a strong rule based classification algorithm which is used only for mining static small data sets. If the genetic algorithm can be made scalable and adaptable by reducing its I/O intensity, it will become an efficient and effective tool for mining large data sets like data streams. In this paper a scalable and adaptable online genetic algorithm is proposed to mine classification rules for the data streams with concept drifts. Since the data streams are generated continuously in a rapid rate, the proposed method does not use a fixed static data set for fitness calculation. Instead, it extracts a small snapshot of the training example from the current part of data stream whenever data is required for the fitness calculation. The proposed method also builds rules for all the classes separately in a parallel independent iterative manner. This makes the proposed method scalable to the data streams and also adaptable to the concept drifts that occur in the data stream in a fast and more natural way without storing the whole stream or a part of the stream in a compressed form as done by the other rule based algorithms. The results of the proposed method are comparable with the other standard methods which are used for mining the data streams.