Classifier systems and genetic algorithms
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Genetic Algorithms in Machine Learning
Machine Learning and Its Applications, Advanced Lectures
An Empirical Comparison of Selection Methods in Evolutionary Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
Proceedings of the 6th ACM international conference on Image and video retrieval
Application of elitist multi-objective genetic algorithm for classification rule generation
Applied Soft Computing
Incremental clustering of dynamic data streams using connectivity based representative points
Data & Knowledge Engineering
A hybrid model using genetic algorithm and neural network for classifying garment defects
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Decision support for proposal grouping: A hybrid approach using knowledge rule and genetic algorithm
Expert Systems with Applications: An International Journal
The Pre-FUFP algorithm for incremental mining
Expert Systems with Applications: An International Journal
A new intelligent diagnosis system for the heart valve diseases by using genetic-SVM classifier
Expert Systems with Applications: An International Journal
Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap
Computational Statistics & Data Analysis
Rule induction based on an incremental rough set
Expert Systems with Applications: An International Journal
Optimized fuzzy classification using genetic algorithm
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Application of Genetic Algorithm in unit selection for Malay speech synthesis system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Traditionally, data mining tasks such as classification and clustering are performed on data warehouses. Usually, updates are collected and applied to the data warehouse frequent time periods. For this reason, all patterns derived from the data warehouse have to be updated frequently as well. Due to the very large volumes of data, it is highly desirable to perform these updates incrementally. This study proposes a new incremental genetic algorithm for classification for efficiently handling new transactions. It presents the comparison results of traditional genetic algorithm and incremental genetic algorithm for classification. Experimental results show that our incremental genetic algorithm considerably decreases the time needed for training to construct a new classifier with the new dataset. This study also includes the sensitivity analysis of the incremental genetic algorithm parameters such as crossover probability, mutation probability, elitism and population size. In this analysis, many specific models were created using the same training dataset but with different parameter values, and then the performances of the models were compared.