The aim of the talk is just to give the information and general knowledge about three popular Machine Learning tools: WEKA, MEKA, and MOA. One of the main advantages of these tools, for non-experienced users, is that all of them can be used without need of programming by using the GUI.
WEKA is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
On-line course for learning how to use WEKA: https://www.cs.waikato.ac.nz/ml/weka/mooc/dataminingwithweka/
Resources:
- Main page: https://www.cs.waikato.ac.nz/ml/weka/
- Data sets: https://www.cs.waikato.ac.nz/ml/weka/datasets.html
MOA is the most popular open source framework for data stream mining, with a very active growing community (blog). It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems.
Some tutorials on its webpage: https://moa.cms.waikato.ac.nz/tutorial-1-introduction-to-moa/
Resources:
- Main page: https://moa.cms.waikato.ac.nz/
- Data sets: https://moa.cms.waikato.ac.nz/datasets/
The MEKA project provides an open source implementation of methods for multi-label learning and evaluation. In multi-label classification, we want to predict multiple output variables for each input instance. This different from the 'standard' case (binary, or multi-class classification) which involves only a single target variable. MEKA is based on the WEKA Machine Learning Toolkit; it includes dozens of multi-label methods from the scientific literature, as well as a wrapper to the related MULAN framework.
A tutorial for introducing MEKA: https://sourceforge.net/projects/meka/files/meka-1.9.1/Tutorial.pdf
Resources:
- Main page: http://waikato.github.io/meka/
- Data sets: http://waikato.github.io/meka/datasets/ and http://www.uco.es/kdis/mllresources/