Leveraging Neural Networks and Calibration Measures for Confident Feature Selection

IF 5.3 3区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hassan Gharoun;Navid Yazdanjue;Mohammad Sadegh Khorshidi;Fang Chen;Amir H. Gandomi
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引用次数: 0

Abstract

With the surge in data generation, both vertically (i.e., volume of data) and horizontally (i.e., dimensionality) the burden of the curse of dimensionality has become increasingly palpable. Feature selection, a key facet of dimensionality reduction techniques, has advanced considerably to address this challenge. One such advancement is the Boruta feature selection algorithm, which successfully discerns meaningful features by contrasting them to their permutated counterparts known as shadow features. Building on this, this paper introduces NeuroBoruta, that extends the traditional Boruta approach by integrating neural networks and calibration metrics to improve prediction accuracy and reduce model uncertainty. By augmenting shadow features with noise and utilizing neural network-based perturbation for importance evaluation, and further incorporating calibration metrics alongside accuracy this evolved version of the Boruta method is presented. Experimental results demonstrate that NeuroBoruta significantly enhances the predictive performance and reliability of classification models across various datasets, including medical imaging and standard UCI datasets. This study underscores the importance of considering both feature relevance and model uncertainty in the feature selection process, particularly in domains requiring high accuracy and reliability.
利用神经网络和校准措施进行自信特征选择
随着数据生成的激增,无论是垂直(即数据量)还是水平(即维度),维度诅咒的负担变得越来越明显。特征选择是降维技术的一个关键方面,在解决这一挑战方面已经取得了相当大的进展。其中一个进步是Boruta特征选择算法,它通过将它们与排列的对应特征(即阴影特征)进行对比,成功地识别出有意义的特征。在此基础上,本文介绍了NeuroBoruta,它通过集成神经网络和校准度量来扩展传统的Boruta方法,以提高预测精度并降低模型的不确定性。通过用噪声增强阴影特征,利用基于神经网络的扰动进行重要性评估,并进一步结合校准指标以及准确性,提出了Boruta方法的进化版本。实验结果表明,NeuroBoruta显著提高了各种数据集(包括医学影像和标准UCI数据集)分类模型的预测性能和可靠性。该研究强调了在特征选择过程中同时考虑特征相关性和模型不确定性的重要性,特别是在需要高精度和可靠性的领域。
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来源期刊
CiteScore
10.30
自引率
7.50%
发文量
147
期刊介绍: The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys. TETCI is an electronics only publication. TETCI publishes six issues per year. Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.
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