- Title
- FlexDM: simple, parallel and fault-tolerant data mining using WEKA
- Creator
- Flannery, Madison; Budden, David M.; Mendes, Alexandre
- Relation
- Source Code for Biology and Medicine Vol. 10
- Publisher Link
- http://dx.doi.org/10.1186/s13029-015-0045-3
- Publisher
- BioMed Central
- Resource Type
- journal article
- Date
- 2015
- Description
- Background: With the continued exponential growth in data volume, large-scale data mining and machine learning experiments have become a necessity for many researchers without programming or statistics backgrounds. WEKA (Waikato Environment for Knowledge Analysis) is a gold standard framework that facilitates and simplifies this task by allowing specification of algorithms, hyper-parameters and test strategies from a streamlined Experimenter GUI. Despite its popularity, the WEKA Experimenter exhibits several limitations that we address in our new FlexDM software. Results: FlexDM addresses four fundamental limitations with the WEKA Experimenter: reliance on a verbose and difficult-to-modify XML schema; inability to meta-optimise experiments over a large number of algorithm hyper-parameters; inability to recover from software or hardware failure during a large experiment; and failing to leverage modern multicore processor architectures. Direct comparisons between the FlexDM and default WEKA XML schemas demonstrate a 10-fold improvement in brevity for a specification that allows finer control of experimental procedures. The stability of FlexDM has been tested on a large biological dataset (approximately 450¿k attributes by 150 samples), and automatic parallelisation of tasks yields a quasi-linear reduction in execution time when distributed across multiple processor cores. Conclusion: FlexDM is a powerful and easy-to-use extension to the WEKA package, which better handles the increased volume and complexity of data that has emerged during the 20¿years since WEKA's original development. FlexDM has been tested on Windows, OSX and Linux operating systems and is provided as a pre-configured virtual reference environment for trivial usage and extensibility. This software can substantially improve the productivity of any research group conducting large-scale data mining or machine learning tasks, in addition to providing non-programmers with improved control over specific aspects of their data analysis pipeline via a succinct and simplified XML schema.
- Subject
- data mining; machine learning; XML; Java; WEKA
- Identifier
- http://hdl.handle.net/1959.13/1313968
- Identifier
- uon:22675
- Identifier
- ISSN:1751-0473
- Language
- eng
- Full Text
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