• Publications
  • Influence
Rotation Forest: A New Classifier Ensemble Method
TLDR
We propose a method for generating classifier ensembles based on feature extraction based on rotation of the feature axes. Expand
  • 1,459
  • 146
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Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
TLDR
Diversity among the members of a team of classifiers is deemed to be a key issue in classifier combination. Expand
  • 1,878
  • 126
  • PDF
Combining Pattern Classifiers: Methods and Algorithms
TLDR
Thank you for downloading combining pattern classifiers methods and algorithms, but end up in infectious downloads. Expand
  • 2,388
  • 109
  • PDF
Decision templates for multiple classifier fusion: an experimental comparison
TLDR
We compare 11 versions of our model with 14 other techniques for classi"er fusion on both data sets. Expand
  • 957
  • 66
  • PDF
A Theoretical Study on Six Classifier Fusion Strategies
  • L. Kuncheva
  • Computer Science, Mathematics
  • IEEE Trans. Pattern Anal. Mach. Intell.
  • 1 February 2002
TLDR
We look at a single point in feature space, two classes, and L classifiers estimating the posterior probability for class /spl omega//sub 1/. Expand
  • 700
  • 40
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A stability index for feature selection
  • L. Kuncheva
  • Computer Science
  • Artificial Intelligence and Applications
  • 12 February 2007
TLDR
A consistency index is proposed here to help feature selection when multiple selection sequences are available. Expand
  • 352
  • 40
  • PDF
Fuzzy Classifier Design
  • L. Kuncheva
  • Computer Science
  • Studies in Fuzziness and Soft Computing
  • 19 May 2000
TLDR
This book about fuzzy classifier design briefly introduces the fundamentals of supervised pattern recognition and fuzzy set theory. Expand
  • 457
  • 29
Measures of Diversity in Classifier Ensembles
  • 339
  • 28
Classifier Ensembles for Changing Environments
  • L. Kuncheva
  • Computer Science
  • Multiple Classifier Systems
  • 9 June 2004
TLDR
We consider strategies for building classifier ensembles for non-stationary environments where the classification task changes during the operation of the ensemble. Expand
  • 345
  • 25
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Relationships between combination methods and measures of diversity in combining classifiers
TLDR
We look at the relationships between different methods of classifier combination and different measures of diversity in the ensemble. Expand
  • 269
  • 22
  • PDF