{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,2]],"date-time":"2023-09-02T04:52:12Z","timestamp":1693630332689},"reference-count":13,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,10,24]],"date-time":"2019-10-24T00:00:00Z","timestamp":1571875200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,10,24]],"date-time":"2019-10-24T00:00:00Z","timestamp":1571875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"published-print":{"date-parts":[[2020,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n<jats:p>Great amount of stored information used in connection with Machine Learning and statistical methods enables high quality insight and analysis of data that leads to design of high precision predictive and classification systems. In the process of analysis, selection of most informative features is crucial for later quality of the designed system. In this report, we propose two implementations of multidimensional feature selection (MDFS) algorithm (Piliszek et\u00a0al. in Mdfs-multidimensional feature selection. arXiv preprint. <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/arxiv.org\/abs\/1811.00631\">arXiv:1811.00631<\/jats:ext-link>, 2018) that can be used in distributed environments for detection of all-relevant variables in data sets with discrete decision variable. While most methods discard information about interactions between features, MDFS is designed towards identification of informative variables that are not relevant when considered alone but are relevant in groups. We have developed software using C++ and High Performance ParalleX (HPX) (Kaiser et\u00a0al. in STEllAR-GROUP\/hpx: HPX V1.3.0: the C++ Standards library for parallelism and concurrency. 2019. <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"doi\" xlink:href=\"https:\/\/doi.org\/10.5281\/zenodo.3189323\">10.5281\/zenodo.3189323<\/jats:ext-link>, 2019) to achieve best performance, great scalability and portability. HPX is a library that uses lightweight threads, asynchronous communication, and asynchronous task submission based on the declarative criteria of work. These features enabled us to deeply explore granularity and parallelism of the MDFS algorithm. Software is prepared entirely in C++; therefore, calculations can be performed using CPUs on desktops, distributed systems, and any system with C++ compiler support. During testing on Cray XC40 (Okeanos) using artificially prepared data, we achieved 196 times acceleration on 256 nodes compared to a single node. From this point, ICM computing facility is capable of massively parallel feature engineering. The main purpose of the software is to enable researchers for more accurate genomics data analysis in search for multiple correlations in potential sources of the diseases.<\/jats:p>","DOI":"10.1007\/s42979-019-0037-5","type":"journal-article","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T01:21:25Z","timestamp":1571966485000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multidimensional Feature Selection and High Performance ParalleX"],"prefix":"10.1007","volume":"1","author":[{"given":"Karol","family":"Niedzielewski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maciej E.","family":"Marchwiany","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Radoslaw","family":"Piliszek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marek","family":"Michalewicz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Witold","family":"Rudnicki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,24]]},"reference":[{"key":"37_CR1","unstructured":"Dua D, Graff C. Uci machine learning repository. \nhttp:\/\/archive.ics.uci.edu\/ml\n\n. 2017. Accessed 18 Oct 2019."},{"key":"37_CR2","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003;3:1157\u201382.","journal-title":"J Mach Learn Res"},{"key":"#cr-split#-37_CR3.1","unstructured":"Guyon I, Gunn S, Ben-Hur A, Dror G. Result analysis of the nips 2003 feature selection challenge. In: L.K. Saul, Y.\u00a0Weiss, L.\u00a0Bottou, editors. Advances in neural information processing systems 17. MIT Press"},{"key":"#cr-split#-37_CR3.2","unstructured":"2005. pp. 545-552.  http:\/\/papers.nips.cc\/paper\/2728-result-analysis-of-the-nips-2003-feature-selection-challenge.pdf  . Accessed 18 Oct 2019."},{"key":"37_CR4","volume-title":"The fourth paradigm: data-intensive scientific discovery","author":"AJ Hey","year":"2009","unstructured":"Hey AJ, Tansley S, Tolle KM, et al. The fourth paradigm: data-intensive scientific discovery, vol. 1. Redmond: Microsoft Research; 2009."},{"key":"37_CR5","doi-asserted-by":"crossref","unstructured":"Kaiser H, Brodowicz M, Sterling T. Parallex an advanced parallel execution model for scaling-impaired applications. In: 2009 International Conference on Parallel Processing Workshops, pp. 394\u2013401. IEEE. 2009.","DOI":"10.1109\/ICPPW.2009.14"},{"key":"37_CR6","doi-asserted-by":"publisher","unstructured":"Kaiser H, aka wash B.A.L, Heller T, Berg A, Simberg M, Biddiscombe J, Bikineev A, Mercer G, Schfer A, Serio A, Kwon T, Huck K, Habraken J, Anderson M, Copik M, Brandt S.R, Stumpf M, Bourgeois D, Blank D, Jakobovits S, Amatya V, Viklund L, Khatami Z, Bacharwar D, Yang S, Diehl P, Schnetter E, Gupta N, Wagle B. Christopher: STEllAR-GROUP\/hpx: HPX V1.3.0: the C++ Standards library for parallelism and concurrency. 2019. \nhttps:\/\/doi.org\/10.5281\/zenodo.3189323\n\n.","DOI":"10.5281\/zenodo.3189323"},{"issue":"1\u20132","key":"37_CR7","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","volume":"97","author":"R Kohavi","year":"1997","unstructured":"Kohavi R, John GH. Wrappers for feature subset selection. Artif Intell. 1997;97(1\u20132):273\u2013324.","journal-title":"Artif Intell"},{"key":"37_CR8","unstructured":"Mnich K, Rudnicki WR. All-relevant feature selection using multidimensional filters with exhaustive search. CoRR abs\/1705.05756. 2017. \narXiv:abs\/1705.05756\n\n."},{"issue":"8","key":"37_CR9","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1016\/j.tig.2009.06.004","volume":"25","author":"JM P\u00e9rez-P\u00e9rez","year":"2009","unstructured":"P\u00e9rez-P\u00e9rez JM, Candela H, Micol JL. 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