{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T05:10:19Z","timestamp":1695964219575},"reference-count":16,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2007,3,22]],"date-time":"2007-03-22T00:00:00Z","timestamp":1174521600000},"content-version":"vor","delay-in-days":4828,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems &amp;amp; Computers in Japan"],"published-print":{"date-parts":[[1994,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper describes the <jats:italic>a posteriori<\/jats:italic> method of decision tree learning after the tree is applied to a real domain, such as medical diagnoses. Without collecting a new set of diagnosis examples, the presented algorithm reconstructs a decision tree preserving the error rate of diagnosis from an original tree and a frequency of diagnoses, which is counted at reaching the corresponding terminal node of that tree when applied to a real domain. The new tree has a shorter path length to diagnose and a logically same meaning with the original tree because of generating a set of pseudoexamples whose unobserved attribute values uniformly distribute in the value range.<\/jats:p><jats:p>To reduce the computational cost, a method to avoid a generation of a pseudoexample set also is presented. The context dependencies between attributes are considered by introducing an attribute concatenation. The experiments show that an average path length will be reduced by 6 to 10 percent after reconstruction of a randomly generated decision tree with nonoptimized diagnosis frequencies.<\/jats:p>","DOI":"10.1002\/scj.4690250908","type":"journal-article","created":{"date-parts":[[2007,7,8]],"date-time":"2007-07-08T03:22:33Z","timestamp":1183864953000},"page":"93-105","source":"Crossref","is-referenced-by-count":0,"title":["Reconstruction of a decision tree with learning examples generated from an original tree and its characteristics"],"prefix":"10.1002","volume":"25","author":[{"given":"Tohru","family":"Asami","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hachisu","family":"Unoki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuo","family":"Hashimoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seiichi","family":"Yamamoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2007,3,22]]},"reference":[{"key":"e_1_2_1_2_2","first-page":"463","volume-title":"Learning efficient classification procedures and their application to chess end games. 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