/Classification-Complexity-Measures

In this repository, the measures reviewed in article "How Complex is your classification problem? A survey on measuring classification complexity" have been implemented.

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Classification-Complexity-Measures

In this repository, the measures reviewed in article "How Complex is your classification problem? A survey on measuring classification complexity" have been implemented.

Feature-based Measures: overlapping.py

  • Maximum Fisher’s Discriminant Ratio (F1)
  • The Directional-vector Maximum Fisher’s Discriminant Ratio (F1v)
  • Volume of Overlapping Region (F2)
  • Maximum Individual Feature Efficiency (F3)
  • Collective Feature Efficiency (F4)

Measures of Linearity: linearity.py

  • Sum of the Error Distance by Linear Programming (L1)
  • Error Rate of Linear Classifier (L2)
  • Non-Linearity of a Linear Classifier (L3)

Neighborhood Measures: neighborhood.py

  • Fraction of Borderline Points (N1)
  • Ratio of Intra/Extra Class Nearest Neighbor Distance (N2)
  • Error Rate of the Nearest Neighbor Classifier (N3)
  • Non-Linearity of the Nearest Neighbor Classifier (N4)
  • Fraction of Hyperspheres Covering Data (T1)
  • Local Set Average Cardinality (LSC)

Network Measures: network.py

  • Average density of the network (Density)
  • Clustering coefficient (ClsCoef)
  • Hub score (Hubs)

Dimensionality Measures: dimensionality.py

  • Average number of features per dimension (T2)
  • Average number of PCA dimensions per points (T3)
  • Ratio of the PCA Dimension to the Original Dimension (T4)

Class Imbalance Measures: balance.py

  • Entropy of class proportions (C1)
  • Imbalance ratio (C2)

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MIT © Farzaneh Koohestani