Abstract
Many decision tree algorithms were proposed over the last few decades. A lack of publishing standards for decision tree algorithm software produced a large time gap between algorithm proposals and their wider application in practice. Non-existence of common repository for storing algorithms and their parts led to a need to re-implement these algorithms from a scratch when they had to be implemented on a different platform. This makes the comparison between algorithms and their partial improvements vague. In addition, combinations and interactions between different algorithm parts haven't been analyzed thoroughly. Reusable component design of decision tree algorithms has been recently suggested as a potential solution to these problems. In this paper we describe an architecture for component-based (white-box) decision tree algorithm design, and we present an open-source framework which enables design and fair testing of decision tree algorithms and their parts. This architecture and developed platform can provide the research community with a common codebase for storing, designing, and evaluating decision tree algorithms (traditional, multivariate and hybrid) and their partial improvements. It is intended for data mining practitioners, algorithm and software developers, and as well for students, as a technology enhanced learning tool.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal on Artificial Intelligence Tools
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.