Repository for the AdaptiveRandomForest algorithm implemented in MOA 2016-04

My Java codes for the MOA framework. It includes the implementations of FHDDM, FHDDMS, and MDDMs.

Machine Learning algorithms for MOA designed to cope with concept drift.

Repository for the StreamingRandomPatches algorithm implemented in MOA 2019.04

Incremental Gaussian Mixture Network for Non-Stationary Environments

Adaptive Decision Forest(ADF) is an incremental machine learning framework called to produce a decision forest to classify new records. ADF is capable to classify new records even if they are associated with previously unseen classes. ADF also is capable of identifying and handling concept drift; it, however, does not forget previously gained kn…

The implementation of the Diversity Pool algorithm, proposed in the paper "Diversity-Based Pool of Models for Dealing with Recurring Concepts" and presented at IJCNN '18

Distributed Random Forest in Apache Flink

We present a framework called TLF that builds a classifier for the target domain having only few labeled training records by transferring knowledge from the source domain having many labeled records. While existing methods often focus on one issue and leave the other one for the further work, TLF is capable of handling both issues simultaneously…

The implementation of the Concept Drift handling based on Clustering in the Model Space (CDCMS) algorithm, proposed in the paper “A Diversity Framework for Dealing with Multiple Types of Concept Drift Based on Clustering in the Model Space”, accepted by IEEE TNNLS 2020.

Landmark-based Feature Drift Detector

The implementation of Synthetic Minority Oversampling based on stream Clustering (SMOClust)

EDIST2: Error Distance Approach for Drift Detection and Monitoring

A classifier for heterogeneous concept drift inspired in the biologically memory model.

Learning High-Dimensional Evolving Data Streams With Limited Labels