Polishing the Right Apple: Anytime Classification Also Benefits Data Streams with Constant Arrival Times

Classification of items taken from data streams requires algorithms that operate in time sensitive and computationally constrained environments. Often, the available time for classification is not known a priori and may change as a consequence of external circumstances. Many traditional algorithms are unable to provide satisfactory performance while supporting the highly variable response times that exemplify such applications. In such contexts, anytime algorithms, which are amenable to trading time for accuracy, have been found to be exceptionally useful and constitute an area of increasing research activity. Previous techniques for improving anytime classification have generally been concerned with optimizing the probability of correctly classifying individual objects. However, as we shall see, serially optimizing the probability of correctly classifying individual objects K times, generally gives inferior results to batch optimizing the probability of correctly classifying K objects...

• Type: Journal
• Year: 2010
• Where: ICDM
• Authors: Jin Shieh, Eamonn J. Keogh
• Keywords: Algorithms, Data Mining, Data Streams, ICDM 2010, Individual Objects