基于遗传算法特征选择的麻疯树病害识别H2o算法

Rahmat Ramadhani, Triando Hamonangan Saragih, Muhammad Haekal
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引用次数: 0

摘要

麻疯树是一种可以用作柴油替代品的植物。由于农民对麻疯树病害的防治知识缺乏,专家和推广人员数量有限,导致麻疯树质量下降。H2O算法可用于麻疯树病害的识别。基于前人的研究,H2O算法给出96.066%。在本研究中,我们使用遗传算法进行特征选择。带特征选择的H2O算法平均准确率为97.03%,优于不带特征选择的H2O算法。我们得到的参数是种群数量600,交叉率0.8,突变率0.2,迭代次数400。然而,使用特征选择所花费的时间比不使用特征选择所花费的时间要长得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
H2O ALGORITHM FOR JATROPHA CURCAS DISEASE IDENTIFICATION WITH FEATURE SELECTION USING GENETIC ALGORITHM
Jatropha curcas is a plant that can be used as a substitute for diesel fuel. Lack of knowledge of farmers and the limited number of experts and extension agents into the problem of dealing with the disease Jatropha curcas plant which resulted in lower quality of Jatropha curcas. H2O Algorithm can be used for Jatropha Curcas disease identification. Based on previous research, H2O Algorithm gave 96.066%. In this research, we used Genetic Algorithm to do feature selection. H2O algorithm with feature selection gave average accuracy 97.03%, that means were better than without feature selection. The parameters that we got are number of populations 600, crossover rate 0.8 and mutation rate 0.2, and number of iterations 400. However, the time spent using feature selection is so longer than without feature selection.
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