Hybrid Adaboost based on Genetic Algorithm for Gene Expression Data Classification

This paper presents a hybrid Adaboost algorithm. The decision groups are chosen as weak classifiers, which consist of K nearest neighbor algorithm, Naïve Bayes and decision tree. When the weak classifiers are promoted to strong classifier, the genetic algorithm is used to optimize the discourse right of each weak classifier. Experiments show proposed algorithm compared with the weak algorithm integration algorithm with only a single algorithm, the proposed algorithm is superior.