Exploring Early Classification Strategies of Streaming Data with Delayed Attributes

In contrast to traditional machine learning algorithms, where all data are available in batch mode, the new paradigm of streaming data poses additional difficulties, since data samples arrive in a sequence and many hard decisions have to be made on-line. The problem addressed here consists of classifying streaming data which not only are unlabeled, but also have a number l of attributes arriving after some time delay . In this context, the main issues are what to do when the unlabeled incomplete samples and, later on, their missing attributes arrive; when and how to classify these incoming samples; and when and how to update the training set. Three different strategies (for l = 1 and constant ) are explored and evaluated in terms of the accumulated classification error. The results reveal that the proposed on-line strategies, despite their simplicity, may outperform classifiers using only the original, labeled-and-complete samples as a fixed training set. In other words, learning is po...

• Type: Journal
• Year: 2009
• Where: ICONIP
• Authors: Mónica Millán-Giraldo, J. Salvador Sánchez, V. Javier Traver
• Keywords: ICONIP 2009, Incomplete Samples, Information Technology, Suitable On-line Classification, Unlabeled Incomplete Samples