Predicting the rates of cross-pollination between GM and non-GM crops using RBFNN with SVM and bootstrap approach

Chien-Pang Lee, Wen-Shin Lin, Bo-Jein Kuo, Kuo-Chung Chu

Abstract


Recently, genetically modified (GM) technology has been successfully used to reduce the cost and to enhance the profit in agriculture. Although GM technology brings many benefits for non-food crops, people still misgive the effects of GM products for the health and the environment. Furthermore, GM crops might affect food (non-GM) crops in the open environment. Hence, how to find strategies for the coexistence of GM and non-GM crops are become a popular issue. One of the strategies is to determine a befitting distance between GM and non-GM crops to reduce the cross-pollination occurred by predicting the rate of cross-pollination. Owing to most of the existing methods for predicting the cross-pollination rates of non-GM crops are only based on the distance between GM and non-GM crops. To counter this problem, we propose a hybrid method, which is composed of radial basis function neural network (RBFNN), support vector machine (SVM) and bootstrap, to apply in this issue. The proposed method includes three specificities. (a) The proposed method reduces the effect of imbalance class problem. (b) The proposed method uses more variables, which are effect the cross-pollination rates, for prediction to enhance the prediction accuracy. (c) The proposed method searches relevant samples to reduce execution time and enhance the prediction accuracy. The results show the performance of our method is better than the existing methods in terms of the root mean square error (RMSE) in prediction and the correlation coefficient between the actual and the predicted cross-pollination rates.


Full Text: PDF DOI: 10.5430/air.v1n1p84

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Artificial Intelligence Research

ISSN 1927-6974 (Print)   ISSN 1927-6982 (Online)

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