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To address these challenges, the authors propose a hybrid feature-instance-parameter adaptation approach based on matrix factorization, k-nearest neighbors, and self-training. The proposed feature-instance adaptation selects a subset of the source crisis data that is representative for the target crisis data. The selected labeled source data, together with unlabeled target data, are used to learn self-training domain adaptation classifiers for the target crisis. Experimental results have shown that overall the hybrid domain adaptation classifiers perform better than the supervised classifiers learned from the original source data.<\/jats:p>","DOI":"10.4018\/ijiscram.2019070101","type":"journal-article","created":{"date-parts":[[2019,8,13]],"date-time":"2019-08-13T12:58:58Z","timestamp":1565701138000},"page":"1-19","source":"Crossref","is-referenced-by-count":12,"title":["A Hybrid Domain Adaptation Approach for Identifying Crisis-Relevant Tweets"],"prefix":"10.4018","volume":"11","author":[{"given":"Reza","family":"Mazloom","sequence":"first","affiliation":[{"name":"Kansas State University, Manhattan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongmin","family":"Li","sequence":"additional","affiliation":[{"name":"Kansas State University, Manhattan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doina","family":"Caragea","sequence":"additional","affiliation":[{"name":"Kansas State University, Manhattan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cornelia","family":"Caragea","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago, Chicago, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Imran","sequence":"additional","affiliation":[{"name":"Qatar Computing Research Institute, Ar-Rayyan, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJISCRAM.2019070101-0","doi-asserted-by":"publisher","DOI":"10.1145\/3110025.3110164"},{"key":"IJISCRAM.2019070101-1","unstructured":"Ashktorab, Z., Brown, C., Nandi, M., & Culotta, A. 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