The ordering of predictors enables the characterization of observations over intervals of time, in terms of trends, effectively reducing the dimensionality of the input space. Questions relating to the representation, extraction, and evaluation of such features are addressed. A method is proposed to compress, segment, and smooth 2D scatter-plots, using MDL-estimation. The concept of line episode is introduced to capture local trend characteristics. Line episodes can be extracted and used effectively by any classifier using trend-episode analysis. Trends can be described in various scales, with varying levels of detail. The role of scale in the induction of classification models is explored, and a local scaling algorithm for line episodes is developed. Trend-episode analysis is extended to enable any classifier to identify appropriate features at appropriate scales and induce classification models for instances with multiscale representations. The proposed framework is evaluated on synthetic data, under a range of controlled experimental conditions, and on a NASA telemetry monitoring application. The results show that multiscale trend-episode analysis can increase both the separability of classes (leading to more accurate models) and the understandability of models.
Date: Thurs., January 8; Time: 4:15-5:30PM; Place: Gates 100
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