Comparison-Based Random Forests

Haghiri, Siavash; Garreau, Damien; Luxburg, Ulrike von

doi:10.48550/arxiv.1806.06616

Cited by 1 publication

(1 citation statement)

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“…These approaches are hampered by the high computational complexity of their search for exemplars at each node. Similarity Forests [38] and Comparisonbased Random Forests [16] generalise this idea to attribute-value data with random selection of exemplars and developed forests of such trees. Similarity Forests add a cutoff value on the difference in the distance between the two exemplars, and optimizes that cutoff value based on weighted Gini.…”

Section: Decision Tree Approachesmentioning

confidence: 99%

Proximity Forest: an effective and scalable distance-based classifier for time series

Lucas

Shifaz

Pelletier

et al. 2019

Data Min Knowl Disc

137

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Research into the classification of time series has made enormous progress in the last decade. The UCR time series archive has played a significant role in challenging and guiding the development of new learners for time series classification. The largest dataset in the UCR archive holds 10 thousand time series only; which may explain why the primary research focus has been on creating algorithms that have high accuracy on relatively small datasets.This paper introduces Proximity Forest, an algorithm that learns accurate models from datasets with millions of time series, and classifies a time series in milliseconds. The models are ensembles of highly randomized Proximity Trees. Whereas conventional decision trees branch on attribute values (and usually perform poorly on time series), Proximity Trees branch on the proximity of time series to one exemplar time series or another; allowing us to leverage the decades of work into developing relevant measures for time series. Proximity Forest gains both efficiency and accuracy by stochastic selection of both exemplars and similarity measures.Our work is motivated by recent time series applications that provide orders of magnitude more time series than the UCR benchmarks. Our experiments demonstrate that Proximity Forest is highly competitive on the UCR archive: it ranks among the most accurate classifiers while being significantly faster. We demonstrate on a 1M time series Earth observation dataset that Proximity Forest retains this accuracy on datasets that are many orders of magnitude greater than those in the UCR repository, while learning its models at least 100,000 times faster than current state of the art models Elastic Ensemble and COTE.

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Section: Decision Tree Approachesmentioning

confidence: 99%