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The RapidMiner plugin for White-box algorithm design and experimentation.
It is friendly, extendable and free.
Each algorithm can be thought of as a composition of subproblems that needs to be solved. The WhiBo framework allows users to pick among many components that solve each subproblem. This enables users to customize the algoritms for the task at hand. Additionally, each component can be fine-tuned by its parameters. And all this through a friendly graphical user interface, within RapidMiner.
Currently, WhiBo offers components for assembling decision tree, and (soon!) representative-based clustering algorithms. Use WhiBo to analyze what components are responsible for good/bad performance. Learn the parts of algorithms without going into too many details.
Relax while an evolutionary algorithm searches through the algorithm-space, in order to find the best performing composition for the given data. Set the evolutionary strategy parameters, follow the performance evolution in detailed logs, and learn which components bring the survival of the fittest algorithm.
WhiBo plugin is currently developed for RapidMiner 6.
Stay tuned for updates by pressing Join in top right corner.
You can install WhiBo plugin using RapidMiner Marketplace, which is located in Menu bar under Help menu. After opening RapidMiner Marketplace please enter in search box WhiBo and press Search. Once located, please check Select for install/update and press Install x packages. Please read and accept the terms of license and press Install x packages.
If you are interested in using WhiBo package without any background please consult User Guide. However, we highly recommend User Manual where you can find complete tutorial with theoretical description of white box algorithms. If you need help setting up WhiBo RapidMiner process feel free to look at our demo processes on this link and this link.
Finnaly, if you are interested in developing and further extension of WhiBo package you can consult Developer Guide and you can see our GitHub page.
From 2008, numerous papers have shown benefits of white box algorithm design.
[We] showed that using RC based algorithms can allow data mining practitioners to achieve better adaptation for data at hand... One of the advantages of the component-based design approach is that it gives further possibilities for more detailed analysis of RC individual and synergic performance on a dataset which is analyzed in the following subsections.
...large algorithmic space is hard to search since the objective functions are computationally very expensive... Experimental evidence show that utilization of evolutionary algorithms for design of well performing component-based algorithms is useful because it automates the process of finding the appropriate algorithm for data at hand by finding the right component interplay and by adjusting parameters of the assembled components.
[enables] easier development of new decision tree based algorithms that are constructed using RCs found in existing and well known algorithms... generic framework for component-based algorithms design enhances understanding, testing and usability of decision tree algorithm parts.
©2017. Center for Business Decision Making.