Compose: A semisupervised learning framework for initially labeled nonstationary streaming data

Karl B. Dyer, Robert Capo, Robi Polikar

Research output: Contribution to journalArticlepeer-review

81 Scopus citations

Abstract

An increasing number of real-world applications are associated with streaming data drawn from drifting and nonstationary distributions that change over time. These applications demand new algorithms that can learn and adapt to such changes, also known as concept drift. Proper characterization of such data with existing approaches typically requires substantial amount of labeled instances, which may be difficult, expensive, or even impractical to obtain. In this paper, we introduce compacted object sample extraction (COMPOSE), a computational geometry-based framework to learn from nonstationary streaming data, where labels are unavailable (or presented very sporadically) after initialization. We introduce the algorithm in detail, and discuss its results and performances on several synthetic and real-world data sets, which demonstrate the ability of the algorithm to learn under several different scenarios of initially labeled streaming environments. On carefully designed synthetic data sets, we compare the performance of COMPOSE against the optimal Bayes classifier, as well as the arbitrary subpopulation tracker algorithm, which addresses a similar environment referred to as extreme verification latency. Furthermore, using the real-world National Oceanic and Atmospheric Administration weather data set, we demonstrate that COMPOSE is competitive even with a well-established and fully supervised nonstationary learning algorithm that receives labeled data in every batch.

Original languageEnglish (US)
Article number6604410
Pages (from-to)12-26
Number of pages15
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume25
Issue number1
DOIs
StatePublished - Jan 2014

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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