methods

Value Members

1. object DawidSkene

   

   Provides functions for transforming an annotation dataset into a standard label dataset using the DawidSkene algorithm

   Provides functions for transforming an annotation dataset into a standard label dataset using the DawidSkene algorithm

   This algorithm only works with com.enriquegrodrigo.spark.crowd.types.MulticlassAnnotation datasets

   Example:

   1. result: DawidSkeneModel = DawidSkene(dataset)

   Version

   0.1

   See also

   Dawid, Alexander Philip, and Allan M. Skene. "Maximum likelihood estimation of observer error-rates using the EM algorithm." Applied statistics (1979): 20-28.

2. object Glad

   

   Provides functions for transforming an annotation dataset into a standard label dataset using the Glad algorithm

   Provides functions for transforming an annotation dataset into a standard label dataset using the Glad algorithm

   This algorithm only works with com.enriquegrodrigo.spark.crowd.types.BinaryAnnotation datasets

   Example:

   1. result: GladModel = Glad(dataset)

   See also

   Whitehill, Jacob, et al. "Whose vote should count more: Optimal integration of labels from labelers of unknown expertise." Advances in neural information processing systems. 2009.

3. object MajorityVoting

   

   Provides functions for transforming an annotation dataset into a standard label dataset using the majority voting approach

   Provides functions for transforming an annotation dataset into a standard label dataset using the majority voting approach

   This object provides several functions for using majority voting style algorithms over annotations datasets (spark datasets with types com.enriquegrodrigo.spark.crowd.types.BinaryAnnotation, com.enriquegrodrigo.spark.crowd.types.MulticlassAnnotation, or com.enriquegrodrigo.spark.crowd.types.RealAnnotation). For discrete types (com.enriquegrodrigo.spark.crowd.types.BinaryAnnotation, com.enriquegrodrigo.spark.crowd.types.MulticlassAnnotation) the method uses the most frequent class. For continuous types, the mean is used.

   The object also provides methods for estimating the probability of a class for the discrete type, computing, for the binary case, the mean of the positive class and, for the multiclass case, the one vs all mean of a class against the others.

   Example:

   1. result: Dataset[BinaryLabel] = MajorityVoting.transformBinary(dataset)

   Version

   0.1

4. object RaykarBinary

   

   Provides functions for transforming an annotation dataset into a standard label dataset using the RaykarBinary algorithm

   Provides functions for transforming an annotation dataset into a standard label dataset using the RaykarBinary algorithm

   This algorithm only works with com.enriquegrodrigo.spark.crowd.types.BinaryAnnotation datasets

   Example:

   1. result: RaykarBinaryModel  = RaykarBinary(dataset)

   Version

   0.1

   See also

   Raykar, Vikas C., et al. "Learning from crowds." Journal of Machine Learning Research 11.Apr (2010): 1297-1322.

5. object RaykarCont

   

6. object RaykarMulti

   

   Provides functions for transforming an annotation dataset into a standard label dataset using the Raykar algorithm for multiclass

   Provides functions for transforming an annotation dataset into a standard label dataset using the Raykar algorithm for multiclass

   This algorithm only works with com.enriquegrodrigo.spark.crowd.types.MulticlassAnnotation annotation datasets

   Example:

   1. result: RaykarMultiModel = RaykarMulti(dataset, annotations)

   Version

   0.1

   See also

   Raykar, Vikas C., et al. "Learning from crowds." Journal of Machine Learning Research 11.Apr (2010): 1297-1322.