FlexDM: Simple,parallel and fault-tolerant data mining using WEKA

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.

     

Signals of quality and age: the information content of multiple plumage ornaments in male western bluebirds Sialia mexicana

Male structural plumage coloration and UV signals in particular can provide information on individual quality and influence female choice, while melanin-pigmented plumage is largely considered to be important in intrasexual competition. Many avian species demonstrate both types of plumage ornamentation that may convey different information about the signaller's quality or condition in addition to age. We examine rufous and blue plumage ornamentation across multiple body regions in relation to age, condition and reproductive performance in male western bluebirds Sialia mexicana. We demonstrate a strong positive relationship between head plumage brightness and both male age and the mass of the offspring. Older males are in better condition and display a reduced plumage patch on the back while the size of the rufous breast patch increases with increasing condition but not with age. Spectral characters from the wings and rump were not associated with any of the reproductive parameters measured. In conjunction with published evidence showing that females preferentially accept extrapair copulations from older males, these data suggest a need for experimental manipulation of plumage colour in known-aged birds to understand mate choice in this species.     

Language Individuation and Marker Words: Shakespeare and His Maxwell's Demon

Background

Within the structural and grammatical bounds of a common language, all authors develop their own distinctive writing styles. Whether the relative occurrence of common words can be measured to produce accurate models of authorship is of particular interest. This work introduces a new score that helps to highlight such variations in word occurrence, and is applied to produce models of authorship of a large group of plays from the Shakespearean era.

Methodology

A text corpus containing 55,055 unique words was generated from 168 plays from the Shakespearean era (16th and 17th centuries) of undisputed authorship. A new score, CM1, is introduced to measure variation patterns based on the frequency of occurrence of each word for the authors John Fletcher, Ben Jonson, Thomas Middleton and William Shakespeare, compared to the rest of the authors in the study (which provides a reference of relative word usage at that time). A total of 50 WEKA methods were applied for Fletcher, Jonson and Middleton, to identify those which were able to produce models yielding over 90% classification accuracy. This ensemble of WEKA methods was then applied to model Shakespearean authorship across all 168 plays, yielding a Matthews'' correlation coefficient (MCC) performance of over 90%. Furthermore, the best model yielded an MCC of 99%.

Conclusions

Our results suggest that different authors, while adhering to the structural and grammatical bounds of a common language, develop measurably distinct styles by the tendency to over-utilise or avoid particular common words and phrasings. Considering language and the potential of words as an abstract chaotic system with a high entropy, similarities can be drawn to the Maxwell''s Demon thought experiment; authors subconsciously favour or filter certain words, modifying the probability profile in ways that could reflect their individuality and style.