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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Uncharted forest: A technique for exploratory data analysis.

Casey Kneale1, Steven D Brown1

  • 1Department of Chemistry and Biochemistry, University of Delaware, 163 The Green, Newark, DE 19716, USA.

Talanta
|August 9, 2018
PubMed
Summary
This summary is machine-generated.

Uncharted forest, a new unsupervised tree ensemble, aids in visualizing data for classification and provenance studies. It helps understand sample associations and class heterogeneity in high-dimensional datasets.

Keywords:
ClusteringExploratory data analysisProvenanceRandom forest

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Area of Science:

  • Data Science
  • Machine Learning
  • Bioinformatics

Background:

  • Exploratory data analysis is vital for classification models.
  • High-dimensional datasets present challenges in understanding data structures.
  • Provenance studies require effective methods for sample and class association analysis.

Purpose of the Study:

  • To introduce and evaluate the uncharted forest algorithm for exploratory data analysis.
  • To demonstrate its utility in visualizing class and sample associations.
  • To assess its application in classification and provenance studies.

Main Methods:

  • The uncharted forest algorithm partitions data using random variable and metric selections.
  • It builds an ensemble of trees, tallying samples in terminal nodes.
  • A sample association matrix is generated for visualization and probability quantification.

Main Results:

  • The uncharted forest effectively visualizes class associations, sample-sample associations, and class heterogeneity.
  • It aids in identifying uninformative classes for provenance studies.
  • New metrics for quantifying probabilities, considering class membership, were developed and compared.

Conclusions:

  • Uncharted forest offers a novel approach to exploratory data analysis for high-dimensional data.
  • It provides valuable insights into data structure for classification and provenance research.
  • The algorithm's utility and limitations were demonstrated across diverse datasets.