Artifacts using MOA: Massive Online Analysis (15)

The MEKA project provides an open source implementation of methods for multi-label classification and evaluation. It is based on the WEKA Machine Learning Toolkit. Several benchmark methods are also included, as well as the pruned sets and classifier chains methods, other methods from the scientific literature, and a wrapper to the MULAN framework.

Weka implementation of the Ensemble systems for Weka

SEMOSS

service.impl

Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems. This artifact enables you to use MOA from within WEKA.

This artifact includes the MOA learning toolbox into the streams Library

mantis-server-worker

JCLAL is a software system for Active Learning research, developed in the Java programming language. It provides a high-level software framework and a robust object-oriented design that allows an easy use, extention, modification and reusability. It includes the most relevant query strategies proposed on the single-label and multi-label learning contexts.

Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems. This artifact enables you to stream data to MOA from Kafka.

Stream Analysis