Epistasis Blog

From the Computational Genetics Laboratory at the University of Pennsylvania (www.epistasis.org)

Wednesday, December 20, 2017

PMLB: a large benchmark suite for machine learning evaluation and comparison

The paper describing our machine learning benchmark data has been published.

Olson RS, La Cava W, Orzechowski P, Urbanowicz RJ, Moore JH. PMLB: a large benchmark suite for machine learning evaluation and comparison. BioData Min. 2017 Dec 11;10:36. [PDF]

BACKGROUND: The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists.

RESULTS: The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. From this study, we find that existing benchmarks lack the diversity to properly benchmark machine learning algorithms, and there are several gaps in benchmarking problems that still need to be considered.

CONCLUSIONS: This work represents another important step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.

Saturday, December 02, 2017

Relief-Based Feature Selection Methods

We have made multiple improvements to Relief-based methods for feature selection. The power of these approaches is that they are capable of detecting non-additive interactions without a combinatorial algorithm. We have posted two new papers on arXiv documenting our latest work in this area. The first paper is a review while the second presents some new results. The code for these approaches can be found on GitHub.