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Functional gene prediction with vital reduced features: Further topics for feature reduction and evaluation criteria for classifiers

机译:具有重要减少特征的功能基因预测:特征减少和分类器评估标准的其他主题

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Aiming at the prediction of protein solubility, four feature reduction methods are discussed in this paper, including Correlation coefficient method, Filter method, Relief method and Genetic method. With the Top 100 features discovered by genetic method, the best classifier achieves the accuracy of 86% and MCC of 0.7236 in Jackknife test. Moreover, further discussions about feature reduction and classifier reliability evaluation criteria are given. The author claim the exclusive importance of capacity of expansion prediction for classifiers.
机译:针对蛋白质溶解度的预测,本文讨论了四种特征约简方法,包括相关系数法,滤波法,救济法和遗传法。借助遗传方法发现的前100个特征,最佳分类器在Jackknife测试中达到86%的准确度和0.7236的MCC。此外,给出了关于特征约简和分类器可靠性评估标准的进一步讨论。作者声称扩展预测的能力对分类器具有排他性的重要性。

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