BIGDATA: F: New Algorithms of Online Machine Learning for Big Data

BIGDATA：F：大数据在线机器学习的新算法

• 批准号: 1545995
• 负责人: Tianbao Yang
• 金额: $ 71.24万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1545995&HistoricalAwards=false
• 关键词: BIGDATA; New; Algorithms; Online; Machine

This project is developing innovative, theoretically rigorous algorithms to learn from continuously arriving (streaming) data. Specific challenges addressed are class imbalance (one of the concepts to be learned is very rare, as in disease detection), cost constraints on both obtaining features (e.g., computationally expensive image processing), and cost constraints on obtaining class labels (e.g., human annotation.) The algorithms developed in this project make it possible to effectively address big data challenges in streaming data due to increased complexities in various aspects such as heavily imbalanced data distributions, ultrahigh dimensional features, a large number of labels, highly complex constraints, etc. The project will also contribute to training future professionals in big data analytics, including participation in the University of Iowa's undergraduate summer research program and high school student training program.Most work devoted to online learning algorithms and their analysis were developed with the goal of minimizing a symmetric measure (e.g., the classification error) and without considering practical constraints arising in big data. This project addresses imbalanced data by developing online learning algorithms for minimizing asymmetric measures including F-score, area under the ROC curve, and area under precision and recall curve. Convex or non-convex surrogate loss functions that well-approximate these asymmetric measures are constructed and minimized in an online fashion. The project also develops online algorithms under three types of constraints arising in big data context namely constraints on computing costs, on query costs, and complex inequality constraints, by exploring techniques in randomized algorithms, active learning and convex optimization. The developed algorithms are being evaluated in real applications including biomedical semantic indexing, social media mining, and image annotation.

该项目正在开发创新的、理论上严格的算法，以从不断到达的（流）数据中学习。 所解决的具体挑战是类别不平衡（要学习的概念之一非常罕见，如在疾病检测中），获得特征的成本约束（例如，计算上昂贵的图像处理），以及获得类别标签的成本约束（例如，人类注释。） 在这个项目中开发的算法可以有效地解决流数据中的大数据挑战，因为各个方面的复杂性增加，例如严重不平衡的数据分布，多维特征，大量标签，高度复杂的约束等。该项目还将有助于培养未来的大数据分析专业人员，包括参与爱荷华州大学的本科生暑期研究计划和高中生培训计划。大多数致力于在线学习算法及其分析的工作都是以最小化对称度量为目标开发的（例如，分类错误），而不考虑大数据中出现的实际约束。 该项目通过开发在线学习算法来解决不平衡数据，以最大限度地减少不对称措施，包括F-score，ROC曲线下面积以及精确度和召回率曲线下面积。 凸或非凸的替代损失函数，以及近似这些不对称的措施，构造和最小化在一个在线的方式。该项目还通过探索随机算法、主动学习和凸优化技术，在大数据环境中产生的三种约束条件下开发在线算法，即计算成本约束、查询成本约束和复杂不等式约束。 所开发的算法正在真实的应用中进行评估，包括生物医学语义索引，社交媒体挖掘和图像注释。