在大数据中的特征交互检测通过基于新Choquet整体的深度神经网络
Matthew Fried1, Honggang Wang1, Hua Fang2
1Yeshiva University, New York, USA.
概括
这项研究引入了一种新的Choquet Integral激活功能,用于深度神经网络,以分析大数据中的复杂相互作用. 新模型有效地识别了健康数据中的子添加特征相互作用,在各种领域都有应用.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 大数据分析需要先进的算法来进行复杂的特征交互.
- 标准方法往往无法捕捉多特征子集关系.
- 识别协同和对抗关系对于准确的建模至关重要.
研究的目的:
- 为深度神经网络开发一种新的激活功能,以在高维数据中建模复杂的相互作用.
- 为分析加权特征汇编引入一个子添加工具.
- 应用和验证该方法对现实世界健康数据进行体重减轻预测.
主要方法:
- 开发了一种新的Choquet Integral激活功能,用于深度神经网络.
- 将高维数据转换为更简单的子特征集.
- 使用平衡的模糊措施和次添加原则.
- 在与健康相关的数据集上进行测试和超参数优化.
主要成果:
- 巧克特集成激活函数有效地模拟复杂的相互作用和非线性依赖关系.
- 该方法识别了标准方法遗漏的子添加特征相互作用.
- 该模型在使用健康数据的标准基准指标上表现强.
结论:
- 新型激活功能为大数据分析提供了强大的工具,特别是在识别复杂特征交互时.
- 这种方法推进了特征之间的协同和对抗关系的建模.
- 该方法在各种领域具有广泛的适用性,包括生物医学,金融和网络安全.
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