Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. First, as machine learning is becoming more widely-used, we are seeing new applications that do not necessarily have enough labeled data. Second, unlike traditional machine learning where feature engineering is the bottleneck, deep learning techniques automatically generate features, but instead require large amounts of labeled data. Interestingly, recent research in data collection comes not only from the machine learning, natural language, and computer vision communities, but also from the data management community due to the importance of handling large amounts of data. In this survey, we perform a comprehensive study of data collection from a data management point of view. Data collection largely consists of data acquisition, data labeling, and improvement of existing data or models. We provide a research landscape of these operations, provide guidelines on which technique to use when, and identify interesting research challenges. The integration of machine learning and data management for data collection is part of a larger trend of Big data and Artificial Intelligence (AI) integration and opens many opportunities for new research.
11/08/2018 ∙ by Yuji Roh, et al. ∙ 1 ∙ share
The wide use of machine learning is fundamentally changing the software development paradigm (a.k.a. Software 2.0) where data becomes a first-class citizen, on par with code. As machine learning is used in sensitive applications, it becomes imperative that the trained model is accurate, fair, and robust to attacks. While many techniques have been proposed to improve the model training process (in-processing approach) or the trained model itself (post-processing), we argue that the most effective method is to clean the root cause of error: the data the model is trained on (pre-processing). Historically, there are at least three research communities that have been separately studying this problem: data management, machine learning (model fairness), and security. Although a significant amount of research has been done by each community, ultimately the same datasets must be preprocessed, and there is little understanding how the techniques relate to each other and can possibly be integrated. We contend that it is time to extend the notion of data cleaning for modern machine learning needs. We identify dependencies among the data preprocessing techniques and propose MLClean, a unified data cleaning framework that integrates the techniques and helps train accurate and fair models. This work is part of a broader trend of Big data -- Artificial Intelligence (AI) integration.
04/22/2019 ∙ by Ki Hyun Tae, et al. ∙ 0 ∙ share
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