MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.

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.

iGNGSVM, OI-SVM, GNG and ENN developed in MOA/WEKA.

Simple workflow API for MOA.

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…

Repository for the StreamingRandomPatches algorithm implemented in MOA 2019.04

Incremental Gaussian Mixture Network for Non-Stationary Environments

online data stream classification by using MOA(Massive Online Analysis).

Test of the drift classifiers implemented in MOA

Final Repository for Honours Project

VLCS: Vague One-Class Learning and Concept Summarization