Classification Based on Organizational Coevolutionary Algorithm
Jing Liu · Chinese Journal of Computers · 2003
A novel classification method for data mining, Organizational CoEvolutionary algorithm for Classification (OCEC), is proposed in this paper, which is different from the GA based classification methods available. The evolutionary operations of OCEC do not act on rules, but on the given data directly, and rules are extracted from the final evolutionary results, which can avoid generating meaningless rules during evolutionary process. Three evolutionary operators, add and subtract operator, exchange operator and unite operator, and a selection mechanism are developed for organizations. The fitness of organization is then defined based on the importance of attributes, which are determined during evolution. OCEC is compared to other GA based and non GA based classification algorithms on some benchmark datasets from the UCI machine learning repository. Results show the proposed algorithm can achieve higher predicting accuracy and a smaller number of rules. In addition, its performance is more stable in that its predicting accuracy fluctuates in a very small scope during experiments with k fold cross validation method.