Information Technology Reference
In-Depth Information
Chapter 11
Hubness-Aware Classification, Instance
Selection and Feature Construction:
Survey and Extensions to Time-Series
Nenad Tomašev, Krisztian Buza, Kristóf Marussy and Piroska B. Kis
Abstract Time-series classification is the common denominator in many real-world
pattern recognition tasks. In the last decade, the simple nearest neighbor classifier, in
combinationwith dynamic timewarping (DTW) as distancemeasure, has been shown
to achieve surprisingly good overall results on time-series classification problems. On
the other hand, the presence of hubs, i.e., instances that are similar to exceptionally
large number of other instances, has been shown to be one of the crucial properties of
time-series data sets. To achieve high performance, the presence of hubs should be
taken into account formachine learning tasks related to time-series. In this chapter, we
survey hubness-aware classification methods and instance selection, and we propose
to use selected instances for feature construction. We provide detailed description
of the algorithms using uniform terminology and notations. Many of the surveyed
approaches were originally introduced for vector classification, and their application
to time-series data is novel, therefore, we provide experimental results on large
number of publicly available real-world time-series data sets.
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Keywords Time-series classification
Hubs
Instance selection
Feature
construction
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