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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