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

这个项目正在开发创新的、理论上严格的算法，从不断到达的（流）数据中学习。解决的具体挑战是类不平衡（要学习的概念之一非常罕见，如在疾病检测中），获取特征的成本约束（例如，计算昂贵的图像处理），以及获取类标签的成本约束（例如，人类注释）。由于数据分布严重不平衡、超高维特征、大量标签、高度复杂的约束等方面的复杂性增加，本项目开发的算法可以有效地解决流数据中的大数据挑战。该项目还将有助于培养未来的大数据分析专业人员，包括参加爱荷华大学的本科生暑期研究项目和高中生培训项目。大多数致力于在线学习算法及其分析的工作都是为了最小化对称度量（例如，分类误差）而开发的，而没有考虑大数据中产生的实际约束。该项目通过开发在线学习算法来解决不平衡数据，以最小化不对称度量，包括f分数、ROC曲线下面积、精度和召回曲线下面积。凸或非凸替代损失函数，很好地近似这些不对称的措施是构造和最小化在线方式。该项目还通过探索随机算法、主动学习和凸优化技术，在大数据环境中出现的三种约束下开发在线算法，即计算成本约束、查询成本约束和复杂不等式约束。开发的算法正在实际应用中进行评估，包括生物医学语义索引、社交媒体挖掘和图像注释。

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.