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首页> 外文期刊>Indian Journal of Science and Technology >Voting-Boosting: A novel machine learning ensemble for the prediction of Infants' Data
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Voting-Boosting: A novel machine learning ensemble for the prediction of Infants' Data

机译：投票 - 提升：用于预测婴幼儿数据的新型机器学习集合

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Background/Objectives: Owing to the continuous increase of electronic records and recent advances in machine learning, various automated disease diagnosis tools have been developed and proposed in healthcare sector. In the present study, an ensemble methodology using voting and boosting techniques has been proposed for optimal selection of features and prediction of infants' data of India. Methods/Analysis: For feature selection, the best-first search algorithm of wrapper technique has been used in addition to votingboosting. The proposed ensemble consists of combination of heterogeneous classifiers including Random Forest, J48, JRip, CART and Stochastic Gradient Descent (SGD). The effectiveness of the proposed ensemble and single classifiers have been investigated in terms of classification accuracy, precision, f-measure, recall, MCC and PRC area using varied k-fold cross validation. Findings: The results depicted that the proposed Voting-Boosting ensemble (k=15) outperforms the individual classifiers using selected features. Applications / Improvements: The proposed Voting-Boosting ensemble can be extended by using more state-of-the art classification approaches and further utilized for other healthcare datasets for enhancing the performance.
机译：背景/目标：由于电子记录的不断增加和机器学习的最新进步，在医疗部门开发并提出了各种自动化疾病诊断工具。在本研究中，已经提出了一种使用投票和升压技术的集合方法，以获得印度的婴儿数据的特征和预测。方法/分析：对于特征选择，除了投票模具之外还使用了最佳的包装技术搜索算法。该拟议的集合包括异构分类器的组合，包括随机森林，J48，JRIP，推车和随机梯度下降（SGD）。使用各种k折交叉验证，在分类准确度，精度，F测量，召回，MCC和中原地区，已经研究了所提出的集合和单种式分类器的有效性。调查结果：所描绘的结果，所提出的投票升压合奏（k = 15）使用所选功能优于各个分类器。应用程序/改进：通过使用更多最先进的分类方法可以扩展建议的投票促销集合，并进一步用于其他医疗数据集以提高性能。

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