An ensemble method in hybrid real-coded genetic algorithm with pruning for data classification
Hong Zhang, Masumi Ishikawa · Conference on Artificial Intelligence for Applications · 2007
To obtain a classification model with high generalization ability, this paper proposes a novel ensemble method that implements a hybrid real-coded genetic algorithm with pruning (HRGA/P). A crucial idea here is to combine ensemble learning and HRGA/P with parallel computational ability and high generalization ability. Accordingly, the resulting classification model is expected to have high generalization ability. Applications of the proposed method to a wine classification problem well demonstrate its effectiveness. The characteristics of generalization ability of an interpolated model from two classification models are also investigated.