{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T11:14:39Z","timestamp":1709205279129},"reference-count":0,"publisher":"University of Zielona G\u00f3ra, Poland","issue":"3","license":[{"start":{"date-parts":[[2012,9,1]],"date-time":"2012-09-01T00:00:00Z","timestamp":1346457600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The paper presents a new (to the best of the authors\u2019 knowledge) estimator of probability called the \u201cEph \u221a 2 completeness estimator\u201d along with a theoretical derivation of its optimality. The estimator is especially suitable for a small number of sample items, which is the feature of many real problems characterized by data insufficiency. The control parameter of the estimator is not assumed in an a priori, subjective way, but was determined on the basis of an optimization criterion (the least absolute errors).The estimator was compared with the universally used frequency estimator of probability and with Cestnik\u2019s m-estimator with respect to accuracy. The comparison was realized both theoretically and experimentally. The results show the superiority of the Eph \u221a 2 completeness estimator over the frequency estimator for the probability interval p<jats:sub>h<\/jats:sub> \u2208 (0.1, 0.9). The frequency estimator is better for p<jats:sub>h<\/jats:sub> \u2208 [0, 0.1] and p<jats:sub>h<\/jats:sub> \u2208 [0.9, 1].<\/jats:p>","DOI":"10.2478\/v10006-012-0048-z","type":"journal-article","created":{"date-parts":[[2016,3,3]],"date-time":"2016-03-03T11:05:27Z","timestamp":1457003127000},"page":"629-645","source":"Crossref","is-referenced-by-count":2,"title":["Optimal estimator of hypothesis probability for data mining problems with small samples"],"prefix":"10.61822","volume":"22","author":[{"given":"Andrzej","family":"Piegat","sequence":"first","affiliation":[{"name":"Faculty of Computer Science West Pomeranian University of Technology, \u017bo\u0142nierska 49, 71-210 Szczecin, Poland"}]},{"given":"Marek","family":"Landowski","sequence":"additional","affiliation":[{"name":"Institute of Quantitative Methods Maritime University of Szczecin, Wa\u0142y Chrobrego 1\u20132, 70-500 Szczecin, Poland"}]}],"member":"37438","published-online":{"date-parts":[[2012,9,28]]},"container-title":["International Journal of Applied Mathematics and Computer Science"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/content.sciendo.com\/view\/journals\/amcs\/22\/3\/article-p629.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.sciendo.com\/pdf\/10.2478\/v10006-012-0048-z","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T10:29:20Z","timestamp":1709202560000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.sciendo.com\/article\/10.2478\/v10006-012-0048-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,9,1]]},"references-count":0,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2012,9,28]]},"published-print":{"date-parts":[[2012,9,1]]}},"alternative-id":["10.2478\/v10006-012-0048-z"],"URL":"https:\/\/doi.org\/10.2478\/v10006-012-0048-z","relation":{},"ISSN":["2083-8492"],"issn-type":[{"value":"2083-8492","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,9,1]]}}}