Learning with Small Data [conference paper]
Conference
26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - August 23, 2020
Authors
Huaxiu Yao, Xiaowei Jia (Ph.D. 2020), Vipin Kumar (professor), Zhenhui Li
Abstract
In the era of big data, data-driven methods have become increasingly popular in various applications, such as image recognition, traffic signal control, fake news detection. The superior performance of these data-driven approaches relies on large-scale labeled training data, which are probably inaccessible in real-world applications, ie," small (labeled) data" challenge. Examples include predicting emergent events in a city, detecting emerging fake news, and forecasting the progression of conditions for rare diseases. In most scenarios, people care about these small data cases most and thus improving the learning effectiveness of machine learning algorithms with small labeled data has been a popular research topic.
Link to full paper
Keywords
data mining, machine learning