MultiObjective Feature Selection

** There are NEW livestream videos about RapidMiner! Visit my Channel here ** In Lesson 2, I went over the concept of MultiObjective Feature Selection (MOFS). In this lesson we’ll build on MOFS for our model but we’ll forecast the trend and measure it’s accuracy. Revisiting MOFS We learned in lesson 2 that RapidMiner can simultaneously select the best features in your data set while maximizing the performance. We ran the process and the best features were selected below. ...

** There are NEW livestream videos about RapidMiner! Visit my Channel here ** In this tutorial I want to show you how to use MultiObjective Feature Selection (MOFS) in RapidMiner**. **It’s a great technique to simultaneously reduce your attribute set and maximize your performance (hence: MultiObjective). This feature selection process can be run over and over again for your AI Financial Market Model, should it begin to drift. Load in the Process from Tutorial One Start by reading the Building an AI Financial Market Model – Lesson 1 post. At the bottom of that post you can download the RapidMiner process. ...

Before you can begin with building your own AI Financial Market Model (machine learned), you have to decide on what software to use. Since I wrote this article in 2007, many new advances have been made in machine learning. Notably the python module Scikit Learn came out and Hadoop was released into the wild. I’m not overly skilled in coding and programming – I know enough to get by- I settled on RapidMiner. RapidMiner is a very simple visual programming platform that let’s you drag and drop “operators” into a design canvas. Each operator has a specific type of task related to ETL, modeling, scoring, and extending the features of RapidMiner. ...