Release history

Version 0.2

Changes

  • Implemented Label Encoding: labels are no longer required to be integers starting from 0. Categorical (strings) and non-sequential integers are supported (similarly to scikit-learn).
  • Batch processing: Vectorized implementation of predictions. Large speed-up in computation time (100x faster in some cases).
  • Predict proba: only required (in the base estimators) if using methods that rely on probabilities (or if requesting probabilities from the ensemble).
  • Improved documentation: Included additional examples, a step-by-step tutorial on how to use the library.
  • New integration tests: Now covering predict_proba, IH and DFP.
  • Bug fixes on 1) predict_proba 2) KNOP with DFP.

Version 0.1

API

Implemented methods:

  • DES techniques currently available are:
    1. META-DES
    2. K-Nearest-Oracle-Eliminate (KNORA-E)
    3. K-Nearest-Oracle-Union (KNORA-U)
    4. Dynamic Ensemble Selection-Performance(DES-P)
    5. K-Nearest-Output Profiles (KNOP)
    6. Randomized Reference Classifier (DES-RRC)
    7. DES Kullback-Leibler Divergence (DES-KL)
    8. DES-Exponential
    9. DES-Logarithmic
    10. DES-Minimum Difference
    11. DES-Clustering
    12. DES-KNN
  • DCS techniques:
    1. Modified Classifier Rank (Rank)
    2. Overall Locall Accuracy (OLA)
    3. Local Class Accuracy (LCA)
    4. Modified Local Accuracy (MLA)
    5. Multiple Classifier Behaviour (MCB)
    6. A Priori Selection (A Priori)
    7. A Posteriori Selection (A Posteriori)
  • Baseline methods:
    1. Oracle
    2. Single Best
    3. Static Selection
  • Dynamic Frienemy Prunning (DFP)
  • Diversity measures
  • Aggregation functions