{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T11:38:37Z","timestamp":1767008317737,"version":"build-2065373602"},"reference-count":12,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2013,1,24]],"date-time":"2013-01-24T00:00:00Z","timestamp":1358985600000},"content-version":"vor","delay-in-days":389,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc of Assoc for Info"],"published-print":{"date-parts":[[2012,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Social\u2010ecological research is characteristic of long\u2010tail science, with many region\u2010specific studies of social and ecological phenomena that collectively yield a large volume of highly heterogeneous, small data sets. This variability makes it difficult to determine the applicability of a particular data set for a new research question, hindering the reuse of data that has been often collected through extensive effort. In this paper we present results of automatic classification of socio\u2010ecological data into categories defined by a domain model called the SES Framework. We have applied our methods to the classification of a relational database containing over 18 years of research on forest systems. Our preliminary results suggest that decision tree\u2010based classifiers along with textual features perform well at this task. Furthermore, social\u2010ecological data sets are found to exhibit distinct classification features in that the results are promising even for classes that comprise a relatively small portion of the database.<\/jats:p>","DOI":"10.1002\/meet.14504901301","type":"journal-article","created":{"date-parts":[[2013,1,24]],"date-time":"2013-01-24T10:49:23Z","timestamp":1359024563000},"page":"1-4","source":"Crossref","is-referenced-by-count":1,"title":["Mining classifications from social\u2010ecological databases"],"prefix":"10.1002","volume":"49","author":[{"given":"Scott","family":"Jensen","sequence":"first","affiliation":[]},{"given":"Miao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xiaozhong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Beth","family":"Plale","sequence":"additional","affiliation":[]},{"given":"David","family":"Leake","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2013,1,24]]},"reference":[{"key":"e_1_2_7_2_1","unstructured":"Androutsopoulos I. Koutsias J. Chandrinos K.V. Paliouras G. &Spyropoulos C.D.(2000).An evaluation of na\u00efve Bayesian anti\u2010spam filtering. Proceedings of the workshop on Machine Learning in the New Information Age G. Potamias V. Moustakis and M. van Someren (eds.) 11th European Conference on Machine Learning Barcelona Spain pp.9\u201317."},{"key":"e_1_2_7_3_1","doi-asserted-by":"crossref","unstructured":"Chhatre A.andAgrawal A.(2008).Forest commons and local enforcement.Proceedings of the National Academy of Sciences 105(36)13286\u201313291.","DOI":"10.1073\/pnas.0803399105"},{"key":"e_1_2_7_4_1","doi-asserted-by":"crossref","unstructured":"Dumains S.andChen H.(2000).Hierarchical classification of web content inProceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrieval Athens Greece pp.256\u2013263.","DOI":"10.1145\/345508.345593"},{"volume-title":"Text categorization with support vector machines: Learning with many relevant features","year":"1998","author":"Joachims T.","key":"e_1_2_7_5_1"},{"key":"e_1_2_7_6_1","unstructured":"Kashwan P.andKreitmair U.(2010).Forests as Complex SES: Application of PNAS Framework to Forestry. 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