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Robust feature selection based on regularized brownboost loss

机译：基于规则化的Brownboost损失的稳健特征选择

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摘要

Feature selection is an important preprocessing step in machine learning and pattern recognition. It is also a data mining task in some real-world applications. Feature quality evaluation is a key issue when designing an algorithm for feature selection. The classification margin has been used widely to evaluate feature quality in recent years. In this study, we introduce a robust loss function, called Brownboost loss, which computes the feature quality and selects the optimal feature subsets to enhance robustness. We compute the classification loss in a feature space with hypothesis-margin and minimize the loss by optimizing the weights of features. An algorithm is developed based on gradient descent using L_2-norm regularization techniques. The proposed algorithm is tested using UCI datasets and gene expression datasets, respectively. The experimental results show that the proposed algorithm is effective in improving the classification robustness.
机译：特征选择是机器学习和模式识别中重要的预处理步骤。在某些实际应用中，它也是一项数据挖掘任务。在设计特征选择算法时，特征质量评估是一个关键问题。近年来，分类裕度已被广泛用于评估特征质量。在这项研究中，我们引入了一个称为Brownboost损失的鲁棒损失函数，该函数计算特征质量并选择最佳特征子集以增强鲁棒性。我们使用假设余量计算特征空间中的分类损失，并通过优化特征权重将损失最小化。使用L_2范数正则化技术基于梯度下降法开发了一种算法。分别使用UCI数据集和基因表达数据集对提出的算法进行了测试。实验结果表明，该算法在提高分类鲁棒性方面是有效的。

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  • 来源
    《Knowledge-Based Systems》 |2013年第12期|180-198|共19页
  • 作者

    Pan Wei; Qinghua Hu; Peijun Ma; Xiaohong Su;

  • 作者单位

    School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China;

    School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China;

    School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China;

    School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Feature selection; Margin; Robustness; Brownboost loss; Regularization;

    机译：功能选择;余量;坚固性布朗升压损失;正则化;

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