Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Feature Selection with Kernel Class Separability
IEEE Transactions on Pattern Analysis and Machine Intelligence
Anveshan: a framework for analysis of multiple annotators' labeling behavior
LAW IV '10 Proceedings of the Fourth Linguistic Annotation Workshop
Tagging human activities in video by crowdsourcing
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Proceedings of the 2013 International Symposium on Wearable Computers
Automatic assessment of problem behaviour in developmental disabilities
ACM SIGACCESS Accessibility and Computing
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t is challenging to precisely identify the boundary of activities in order to annotate the activity datasets required to train activity recognition systems. This is the case for experts, as well as non-experts who may be recruited for crowd-sourcing paradigms to reduce the annotation effort or speed up the process by distributing the task over multiple annotators. We present a method to automatically adjust annotation boundaries, presuming a correct annotation label, but imprecise boundaries, otherwise known as "label jitter". The approach maximizes the Fukunaga Class-Separability, applied to time series. Evaluations on a standard benchmark dataset showed statistically significant improvements from the initial jittery annotations.