TODO LIST
Urgent:
Design:
- Make set classifier **kwargs as parameters for all algorithms
- Pass a KNN instance as
base_estimatordirectly (as the pattern followed in BaggingEstimator)
Algorithms:
- Steady-State Memetic Algorithm (SSMA) - SETUP FITNESS OPTIONS PARAMETER
- One-Sided Selection (OSS)
- Learning Vector Quantization 1, 2.1 and 3 (LVQ)
- Integrated Concept Prototype Learner 1, 2, 3 and 4 (ICPL)
- Reduction by Space Partioning 1, 2 and 3 (RSP)
- Pairwise Opposite Class Nearest Neighbor (POC-NN)
- Evolutionary Nearest Prototype Classifier (ENPC)
- Particle Swarm Optimization (PSO)
- Adaptive Condensing Algorithm Based on Mixtures Gaussians (MixtGauss)
- Prototype Selection Clonal Selection Algorithm (PSCSA)
- Adaptive Threshold-based Instance Selection Algorithm 1, 2 and 3 (ATISA)
Examples:
- Use example/utils.py for dataset generation, imbalance generation, ratio, ...
- Create individual examples for each algorithm.
- Create an imbalanced datasets example.
Any contribution is more than welcome.