{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T17:46:59Z","timestamp":1780508819123,"version":"3.54.1"},"reference-count":38,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2019,7,31]],"date-time":"2019-07-31T00:00:00Z","timestamp":1564531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2019,7,31]]},"abstract":"<jats:p>Class ambiguity refers to the phenomenon whereby similar features correspond to different classes at different locations. Given heterogeneous geographic data with class ambiguity, the spatial ensemble learning (SEL) problem aims to find a decomposition of the geographic area into disjoint zones such that class ambiguity is minimized and a local classifier can be learned in each zone. The problem is important for applications such as land cover mapping from heterogeneous earth observation data with spectral confusion. However, the problem is challenging due to its high computational cost. Related work in ensemble learning either assumes an identical sample distribution (e.g., bagging, boosting, random forest) or decomposes multi-modular input data in the feature vector space (e.g., mixture of experts, multimodal ensemble) and thus cannot effectively minimize class ambiguity. In contrast, we propose a spatial ensemble framework that explicitly partitions input data in geographic space. Our approach first preprocesses data into homogeneous spatial patches and uses a greedy heuristic to allocate pairs of patches with high class ambiguity into different zones. We further extend our spatial ensemble learning framework with spatial dependency between nearby zones based on the spatial autocorrelation effect. Both theoretical analysis and experimental evaluations on two real world wetland mapping datasets show the feasibility of the proposed approach.<\/jats:p>","DOI":"10.1145\/3337798","type":"journal-article","created":{"date-parts":[[2019,8,13]],"date-time":"2019-08-13T14:41:50Z","timestamp":1565707310000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Spatial Ensemble Learning for Heterogeneous Geographic Data with Class Ambiguity"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3576-6976","authenticated-orcid":false,"given":"Zhe","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Alabama, Tuscaloosa, AL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arpan Man","family":"Sainju","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Alabama, Tuscaloosa, AL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Minnesota, Minneapolis, MN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shashi","family":"Shekhar","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Minnesota, Minneapolis, MN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph","family":"Knight","sequence":"additional","affiliation":[{"name":"Department of Forest Resources, University of Minnesota, North St. Paul, MN"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,8,12]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Weka 3: Data mining software in Java. Retrieved on","year":"2018","unstructured":"2016. Weka 3: Data mining software in Java. Retrieved on September 1, 2018 from http:\/\/www.cs.waikato.ac.nz\/ml\/weka\/. 2016. Weka 3: Data mining software in Java. Retrieved on September 1, 2018 from http:\/\/www.cs.waikato.ac.nz\/ml\/weka\/."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1018054314350"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/648054.743935"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.112"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2012.2197589"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcs.2013.03.014"},{"key":"e_1_2_1_9_1","volume-title":"Geographically Weighted Regression","author":"Fotheringham A. Stewart","unstructured":"A. Stewart Fotheringham , Chris Brunsdon , and Martin Charlton . 2003. Geographically Weighted Regression . John Wiley 8 Sons, Limited. A. Stewart Fotheringham, Chris Brunsdon, and Martin Charlton. 2003. Geographically Weighted Regression. John Wiley 8 Sons, Limited."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.1997.1504"},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of the 24th International Conference on Artificial Intelligence. AAAI Press, 3525--3531","author":"Gon\u00e7alves Andr\u00e9 R.","year":"2015","unstructured":"Andr\u00e9 R. Gon\u00e7alves , Fernando J. Von Zuben , and Arindam Banerjee . 2015 . Multi-label structure learning with ising model selection . In Proceedings of the 24th International Conference on Artificial Intelligence. AAAI Press, 3525--3531 . Andr\u00e9 R. Gon\u00e7alves, Fernando J. Von Zuben, and Arindam Banerjee. 2015. Multi-label structure learning with ising model selection. In Proceedings of the 24th International Conference on Artificial Intelligence. AAAI Press, 3525--3531."},{"key":"e_1_2_1_12_1","volume-title":"Proceedings of the Technical Symposium East. International Society for Optics and Photonics, 2--9.","author":"Robert","unstructured":"Robert M. Haralick and Linda G. Shapiro. 1985. Image segmentation techniques . In Proceedings of the Technical Symposium East. International Society for Optics and Photonics, 2--9. Robert M. Haralick and Linda G. Shapiro. 1985. Image segmentation techniques. In Proceedings of the Technical Symposium East. International Society for Optics and Photonics, 2--9."},{"key":"e_1_2_1_13_1","doi-asserted-by":"crossref","unstructured":"G. J. Hay and G. Castilla. 2008. Geographic object-based image analysis (GEOBIA): A new name for a new discipline. In Object-based Image Analysis. Springer 75--89.  G. J. Hay and G. Castilla. 2008. Geographic object-based image analysis (GEOBIA): A new name for a new discipline. In Object-based Image Analysis. Springer 75--89.","DOI":"10.1007\/978-3-540-77058-9_4"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.990132"},{"key":"e_1_2_1_15_1","volume-title":"Data Complexity in Pattern Recognition","author":"Ho Tin Kam","unstructured":"Tin Kam Ho , Mitra Basu , and Martin Hiu Chung Law . 2006. Measures of geometrical complexity in classification problems . In Data Complexity in Pattern Recognition . Springer , 1--23. Tin Kam Ho, Mitra Basu, and Martin Hiu Chung Law. 2006. Measures of geometrical complexity in classification problems. In Data Complexity in Pattern Recognition. Springer, 1--23."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1991.3.1.79"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3139958.3140044"},{"key":"e_1_2_1_18_1","volume-title":"Spatial Big Data Science: Classification Techniques for Earth Observation Imagery","author":"Jiang Zhe","unstructured":"Zhe Jiang and Shashi Shekhar . 2017. Spatial Big Data Science: Classification Techniques for Earth Observation Imagery . Springer . Zhe Jiang and Shashi Shekhar. 2017. Spatial Big Data Science: Classification Techniques for Earth Observation Imagery. Springer."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2424321.2424372"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2013.96"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2014.2373383"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2012.2198654"},{"key":"e_1_2_1_23_1","volume-title":"Proceedings of the SIAM International Conference on Data Mining. SIAM, 253--261","author":"Karpatne Anuj","year":"2014","unstructured":"Anuj Karpatne , Ankush Khandelwal , Shyam Boriah , and Vipin Kumar . 2014 . Predictive learning in the presence of heterogeneity and limited training data . In Proceedings of the SIAM International Conference on Data Mining. SIAM, 253--261 . Anuj Karpatne, Ankush Khandelwal, Shyam Boriah, and Vipin Kumar. 2014. Predictive learning in the presence of heterogeneity and limited training data. In Proceedings of the SIAM International Conference on Data Mining. SIAM, 253--261."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974010.82"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1080\/01431160600746456"},{"key":"e_1_2_1_26_1","volume-title":"Martin et al","author":"David","year":"2002","unstructured":"David R. Martin et al . 2002 . Matlab codes for multi-class hierarchical mixture of experts model. Retrieved from: http:\/\/www.ics.uci.edu\/&sim;fowlkes\/software\/hme\/. David R. Martin et al. 2002. Matlab codes for multi-class hierarchical mixture of experts model. Retrieved from: http:\/\/www.ics.uci.edu\/&sim;fowlkes\/software\/hme\/."},{"key":"e_1_2_1_27_1","unstructured":"Barbara Pease and Allan Pease. 2006. The Definitive Book of Body Language. Bantam.  Barbara Pease and Allan Pease. 2006. The Definitive Book of Body Language. Bantam."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1080\/01431160512331316838"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.1996.550800"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.14358\/PERS.80.5.439"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2015.2471235"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_1_33_1","volume-title":"Proceedings of the IEEE International Workshop on Content-Based Access of Image and Video Database. IEEE, 42--51","author":"Szummer Martin","unstructured":"Martin Szummer and Rosalind W. Picard . 1998. Indoor-outdoor image classification . In Proceedings of the IEEE International Workshop on Content-Based Access of Image and Video Database. IEEE, 42--51 . Martin Szummer and Rosalind W. Picard. 1998. Indoor-outdoor image classification. In Proceedings of the IEEE International Workshop on Content-Based Access of Image and Video Database. IEEE, 42--51."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.2307\/143141"},{"key":"e_1_2_1_35_1","volume-title":"Proceedings of the Conference on Neural Information Processing Systems","author":"Xu Lei","year":"1995","unstructured":"Lei Xu , Michael I. Jordan , and Geoffrey E. Hinton . 1995. An alternative model for mixtures of experts . In Proceedings of the Conference on Neural Information Processing Systems ( 1995 ), 633--640. Lei Xu, Michael I. Jordan, and Geoffrey E. Hinton. 1995. An alternative model for mixtures of experts. In Proceedings of the Conference on Neural Information Processing Systems (1995), 633--640."},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1137\/1037125"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2200299"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1201\/b12207"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3337798","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3337798","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:54:25Z","timestamp":1750204465000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3337798"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,31]]},"references-count":38,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,7,31]]}},"alternative-id":["10.1145\/3337798"],"URL":"https:\/\/doi.org\/10.1145\/3337798","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,31]]},"assertion":[{"value":"2018-03-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-05-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-08-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}