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

Xiaochuan Ai, Jingbo Xia
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Abstract

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.
具有重要约简特征的功能基因预测:分类器的特征约简和评估标准的进一步主题
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、缓解法和遗传法。利用遗传方法发现的前100个特征,最佳分类器在Jackknife测试中准确率达到86%,MCC为0.7236。进一步讨论了特征约简和分类器可靠性评价准则。作者提出了扩展预测能力对分类器的唯一重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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