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，以从连续到达（流）数据中学习。 所解决的具体挑战是阶级不平衡（在疾病检测中，要学习的概念之一非常罕见），在获得功能（例如，计算上昂贵的图像处理）上都存在成本限制，并且成本的限制因素而在类似数据中获得了众多数据，因此可以在逐渐挑战的各个方面挑战，从而获得了综合数据，因此获得了类标签（例如，人类注释）。分布，超高维度，大量标签，高度复杂的约束等。该项目还将有助于培训未来的专业人员在大数据分析中的培训，包括参与爱荷华大学的本科夏季研究计划和高中生培训计划。大多数工作，致力于在线学习算法及其分析的大多数级别的范围，而没有对级别的衡量标准进行，并确定了衡量标准（E. e.g）。大数据出现。 该项目通过开发在线学习算法来解决不平衡数据，以最大程度地减少不对称措施，包括F-SCORE，ROC曲线下的区域以及精确度和回忆曲线的区域。 以在线方式构建和最小化这些不对称措施的凸或非凸替代损失功能。该项目还通过在计算成本，查询成本和复杂的不平等限制的三种限制下，在三种类型的约束下开发在线算法，通过探索随机算法，主动学习和convex优化的技术。 在实际应用中评估了开发的算法，包括生物医学语义索引，社交媒体挖掘和图像注释。