Ensemble Learners

In addition to making predictions using individual classification algorithms, ML-Flex can also make predictions using various ensemble-learning methods. These methods combine evidence across multiple individual predictions to derive an aggregate prediction. If multiple data processors and/or algorithms are specified in the experiment settings, ensemble learners will aggregate evidence across all combinations of these; when only one combination exists, ensemble learning will not be performed.

Below is a short description of each ensemble-learning approach:

Table of Contents

Introduction to ML-Flex


Configuring Algorithms

Creating an Experiment File

List of Experiment Settings

Running an Experiment

List of Command-line Arguments

Executing Experiments Across Multiple Computers

Modifying Java Source Code

Creating a New Data Processor

Third-party Machine Learning Software

Integrating with Third-party Machine Learning Software

About Ensemble Learners