API Reference

This is the full API documentation of the DESlib. Currently the library is divided into four modules:

Dynamic Classifier Selection (DCS)

This module contains the implementation of techniques in which only the base classifier that attained the highest competence level is selected for the classification of the query.

The deslib.dcs provides a set of key dynamic classifier selection algorithms (DCS).

Dynamic Ensemble Selection (DES)

Dynamic ensemble selection strategies refer to techniques that select an ensemble of classifier rather than a single one. All base classifiers that attain a minimum competence level are selected to compose the ensemble of classifiers.

The deslib.des provides a set of key dynamic ensemble selection algorithms (DES).

Static ensembles

This module provides the implementation of static ensemble techniques that are usually used as a baseline for the comparison of DS methods: Single Best (SB), Static Selection (SS), Stacked classifier and Oracle.

The deslib.static provides a set of static ensemble methods which are often used as a baseline to compare the performance of dynamic selection algorithms.


Utility functions for ensemble methods such as diversity and aggregation methods.

The deslib.util This module includes various utilities. They are divided into four parts:

deslib.util.aggregation - Implementation of aggregation functions such as majority voting and averaging. Such functions can be applied to any list of classifiers.

deslib.util.diversity - Implementation of different measures of diversity between classifiers.

deslib.util.prob_functions - Functions to estimate the competence of a base classifier based on the probability estimates.

deslib.util.instance_hardness - Functions to measure the hardness level of a given instance

deslib.util.faiss_knn_wrapper - Wrapper for Facebook AI fast similarity search on GPU

deslib.util.datasets - Provides methods to generate synthetic data.

deslib.util.knne - Implementation of the K-Nearest Neighbors Equality technique