Release history¶
Version 0.2¶
- Second release of the stable API. By Rafael M O Cruz and Luiz G Hafemann.
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¶
- First release of the stable API. By Rafael M O Cruz and Luiz G Hafemann.
Implemented methods:¶
- DES techniques currently available are:
- META-DES
- K-Nearest-Oracle-Eliminate (KNORA-E)
- K-Nearest-Oracle-Union (KNORA-U)
- Dynamic Ensemble Selection-Performance(DES-P)
- K-Nearest-Output Profiles (KNOP)
- Randomized Reference Classifier (DES-RRC)
- DES Kullback-Leibler Divergence (DES-KL)
- DES-Exponential
- DES-Logarithmic
- DES-Minimum Difference
- DES-Clustering
- DES-KNN
- DCS techniques:
- Modified Classifier Rank (Rank)
- Overall Locall Accuracy (OLA)
- Local Class Accuracy (LCA)
- Modified Local Accuracy (MLA)
- Multiple Classifier Behaviour (MCB)
- A Priori Selection (A Priori)
- A Posteriori Selection (A Posteriori)
- Baseline methods:
- Oracle
- Single Best
- Static Selection
- Dynamic Frienemy Prunning (DFP)
- Diversity measures
- Aggregation functions