{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T05:41:08Z","timestamp":1768542068127,"version":"3.49.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T00:00:00Z","timestamp":1715904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T00:00:00Z","timestamp":1715904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Geoinformatica"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s10707-024-00523-x","type":"journal-article","created":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T08:01:40Z","timestamp":1715932900000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A spatial dependency based reinforcement learning model for selecting features in spatial classification"],"prefix":"10.1007","volume":"29","author":[{"given":"Cheng","family":"Wei","sequence":"first","affiliation":[]},{"given":"Wenhao","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,17]]},"reference":[{"issue":"1","key":"523_CR1","first-page":"1","volume":"4","author":"AN Alharbi","year":"2020","unstructured":"Alharbi AN, Dahab M (2020) An improvement in branch and bound algorithm for feature selection. Int J Inf Technol Lang Stud 4(1):1\u201311","journal-title":"Int J Inf Technol Lang Stud"},{"issue":"2","key":"523_CR2","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1111\/j.1461-0248.2009.01422.x","volume":"13","author":"CM Beales","year":"2010","unstructured":"Beales CM et al (2010) Regression analysis of spatial data. Ecol Lett 13(2):246\u2013264","journal-title":"Ecol Lett"},{"issue":"2","key":"523_CR3","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s10506-020-09270-4","volume":"29","author":"A Bibal","year":"2021","unstructured":"Bibal A et al (2021) Legal requirements on explainability in machine learning. Artif Intell Law 29(2):149\u2013169","journal-title":"Artif Intell Law"},{"key":"523_CR4","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1613\/jair.1.12228","volume":"70","author":"N Burkart","year":"2021","unstructured":"Burkart N, Huber MF (2021) A survey on the explainability of supervised machine learning. J Artif Intell Res 70:245\u2013317. https:\/\/doi.org\/10.1613\/jair.1.12228","journal-title":"J Artif Intell Res"},{"issue":"2","key":"523_CR5","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1890\/1540-9295(2007)5[80:SHIUER]2.0.CO;2","volume":"5","author":"ML Cadenasso","year":"2007","unstructured":"Cadenasso ML, Pickett STA, Schwarz K (2007) Spatial heterogeneity in urban ecosystems: reconceptualizing land cover and a framework for classification. Front Ecol Environ 5(2):80\u201388","journal-title":"Front Ecol Environ"},{"key":"523_CR6","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.rse.2012.09.005","volume":"127","author":"A Comber","year":"2012","unstructured":"Comber A et al (2012) Spatial analysis of remote sensing image classification accuracy. Remote Sens Environ 127:237\u2013246. https:\/\/doi.org\/10.1016\/j.rse.2012.09.005","journal-title":"Remote Sens Environ"},{"issue":"6","key":"523_CR7","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1016\/j.compenvurbsys.2009.07.008","volume":"33","author":"S Dodge","year":"2009","unstructured":"Dodge S, Weibel R, Forootan E (2009) Revealing the physics of movement: comparing the similarity of movement characteristics of different types of moving objects. Comput Environ Urban Syst 33(6):419\u2013434","journal-title":"Comput Environ Urban Syst"},{"key":"523_CR8","doi-asserted-by":"crossref","unstructured":"Ester M, Kriegel HP, Sander J (1997) Spatial data mining: A database approach. In International symposium on spatial databases. Springer, Berlin, Heidelberg, 47\u201366","DOI":"10.1007\/3-540-63238-7_24"},{"issue":"3","key":"523_CR9","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1016\/j.ejor.2017.08.040","volume":"265","author":"B Ghaddar","year":"2018","unstructured":"Ghaddar B, Naoum-Sawaya J (2018) High dimensional data classification and feature selection using support vector machines. Eur J Oper Res 265(3):993\u20131004","journal-title":"Eur J Oper Res"},{"issue":"1","key":"523_CR10","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.patcog.2009.06.009","volume":"43","author":"IA Gheyas","year":"2010","unstructured":"Gheyas IA, Smith LS (2010) Feature subset selection in large dimensionality domains. Pattern Recogn 43(1):5\u201313","journal-title":"Pattern Recogn"},{"key":"523_CR11","doi-asserted-by":"crossref","unstructured":"Gopika N, ME AMK (2018) Correlation based feature selection algorithm for machine learning. In 2018 3rd international conference on communication and electronics systems (ICCES) IEEE, 692\u2013695","DOI":"10.1109\/CESYS.2018.8723980"},{"issue":"3","key":"523_CR12","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/s10707-006-9827-8","volume":"10","author":"Y Huang","year":"2006","unstructured":"Huang Y, Pei J, Xiong H (2006) Mining co-location patterns with rare events from spatial data sets. Geoinformatica 10(3):239\u2013260","journal-title":"Geoinformatica"},{"issue":"4","key":"523_CR13","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1080\/13658816.2019.1684500","volume":"34","author":"K Janowicz","year":"2020","unstructured":"Janowicz K et al (2020) GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. Int J Geogr Inf Sci 34(4):625\u2013636","journal-title":"Int J Geogr Inf Sci"},{"issue":"1","key":"523_CR14","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/36.481894","volume":"34","author":"Y Jhung","year":"1996","unstructured":"Jhung Y, Swain PH (1996) Bayesian contextual classification based on modified M-estimates and Markov random fields. IEEE Trans Geosci Remote Sens 34(1):67\u201375","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"523_CR15","doi-asserted-by":"crossref","unstructured":"Jiao R, Nguyen BH, Xue B et al (2023) A survey on evolutionary multiobjective feature selection in classification: approaches, applications, and challenges. IEEE Trans Evol Comput","DOI":"10.1109\/TEVC.2023.3292527"},{"issue":"2","key":"523_CR16","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1109\/TGRS.2006.885412","volume":"45","author":"LO Jimenez-Rodriguez","year":"2007","unstructured":"Jimenez-Rodriguez LO et al (2007) Unsupervised linear feature-extraction methods and their effects in the classification of high-dimensional data. IEEE Trans Geosci Remote Sens 45(2):469\u2013483","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"3\u20134","key":"523_CR17","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.rse.2005.02.006","volume":"96","author":"T Kasetkasem","year":"2005","unstructured":"Kasetkasem T, Arora MK, Varshney PK (2005) Super-resolution land cover mapping using a Markov random field based approach. Remote Sens Environ 96(3\u20134):302\u2013314","journal-title":"Remote Sens Environ"},{"issue":"1","key":"523_CR18","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s11042-012-1007-2","volume":"71","author":"SK Kim","year":"2014","unstructured":"Kim SK et al (2014) A framework of spatial co-location pattern mining for ubiquitous GIS. Multimedia Tools Appl 71(1):199\u2013218","journal-title":"Multimedia Tools Appl"},{"key":"523_CR19","doi-asserted-by":"publisher","unstructured":"Kunze L et al (2014) Combining top-down spatial reasoning and bottom-up object class recognition for scene understanding. In 2014 IEEE\/RSJ International Conference on Intelligent Robots and Systems IEEE, 2910\u20132915. https:\/\/doi.org\/10.1109\/IROS.2014.6942963","DOI":"10.1109\/IROS.2014.6942963"},{"issue":"1\u20132","key":"523_CR20","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1177\/016001769702000107","volume":"20","author":"JP LeSage","year":"1997","unstructured":"LeSage JP (1997) Bayesian estimation of spatial autoregressive models. Int Reg Sci Rev 20(1\u20132):113\u2013129","journal-title":"Int Reg Sci Rev"},{"key":"523_CR21","doi-asserted-by":"crossref","unstructured":"Lin Y, Chiang YY, Pan F et al (2017) Mining public datasets for modeling intra-city PM2. 5 concentrations at a fine spatial resolution\/\/Proceedings of the 25th ACM SIGSPATIAL international conference on advances in geographic information systems, 1\u201310","DOI":"10.1145\/3139958.3140013"},{"key":"523_CR22","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1109\/ICDM50108.2020.00044","volume":"IEEE","author":"Y Lin","year":"2020","unstructured":"Lin Y, Chiang YY, Franklin M et al (2020) Building autocorrelation-aware representations for fine-scale spatiotemporal prediction. 2020 IEEE Int Conf Data Min (ICDM) IEEE:352\u2013361","journal-title":"2020 IEEE Int Conf Data Min (ICDM)"},{"issue":"4","key":"523_CR23","doi-asserted-by":"publisher","first-page":"242","DOI":"10.3390\/ijgi11040242","volume":"11","author":"X Liu","year":"2022","unstructured":"Liu X, Kounadi O, Zurita-Milla R (2022) Incorporating spatial autocorrelation in machine learning models using spatial lag and eigenvector spatial filtering features. ISPRS Int J Geo-Information 11(4):242","journal-title":"ISPRS Int J Geo-Information"},{"key":"523_CR24","doi-asserted-by":"publisher","first-page":"107933","DOI":"10.1016\/j.patcog.2021.107933","volume":"116","author":"W Ma","year":"2021","unstructured":"Ma W et al (2021) A two-stage hybrid ant colony optimization for high-dimensional feature selection. Pattern Recogn 116:107933","journal-title":"Pattern Recogn"},{"issue":"4","key":"523_CR25","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1080\/13658816.2021.2004602","volume":"36","author":"G Mai","year":"2022","unstructured":"Mai G et al (2022) A review of location encoding for GeoAI: methods and applications. Int J Geogr Inf Sci 36(4):639\u2013673","journal-title":"Int J Geogr Inf Sci"},{"issue":"6","key":"523_CR26","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/j.compenvurbsys.2009.11.001","volume":"33","author":"J Mennis","year":"2009","unstructured":"Mennis J, Guo D (2009) Spatial data mining and geographic knowledge discovery\u2014An introduction. Comput Environ Urban Syst 33(6):403\u2013408","journal-title":"Comput Environ Urban Syst"},{"issue":"6","key":"523_CR27","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1002\/cem.873","volume":"18","author":"AJ Myles","year":"2004","unstructured":"Myles AJ et al (2004) An introduction to decision tree modeling. J Chemometrics: J Chemometrics Soc 18(6):275\u2013285. https:\/\/doi.org\/10.1002\/cem.873","journal-title":"J Chemometrics: J Chemometrics Soc"},{"issue":"12","key":"523_CR28","doi-asserted-by":"publisher","first-page":"2285","DOI":"10.1109\/TKDE.2018.2823740","volume":"30","author":"Z Qi","year":"2018","unstructured":"Qi Z, Wang T, Song G et al (2018) Deep air learning: interpolation, prediction, and feature analysis of fine-grained air quality. IEEE Trans Knowl Data Eng 30(12):2285\u20132297","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"523_CR29","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/BF00116251","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81\u2013106","journal-title":"Mach Learn"},{"issue":"2","key":"523_CR30","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1111\/tgis.12107","volume":"19","author":"A Shariat-Mohaymany","year":"2015","unstructured":"Shariat-Mohaymany A, Shahri M, Mirbagheri B et al (2015) Exploring spatial non\u2010stationarity and varying relationships between crash data and related factors using geographically weighted Poisson regression. Trans GIS 19(2):321\u2013337","journal-title":"Trans GIS"},{"key":"523_CR31","doi-asserted-by":"publisher","unstructured":"Shroff KP, Maheta HH (2015) A comparative study of various feature selection techniques in high-dimensional data set to improve classification accuracy. In 2015 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 1\u20136. https:\/\/doi.org\/10.1109\/ICCCI.2015.7218098","DOI":"10.1109\/ICCCI.2015.7218098"},{"issue":"112","key":"523_CR32","first-page":"1","volume":"21","author":"H Sifaou","year":"2020","unstructured":"Sifaou H, Kammoun A, Alouini MS (2020) High-dimensional linear discriminant analysis classifier for spiked covariance model. J Mach Learn Res 21(112):1\u201324","journal-title":"J Mach Learn Res"},{"key":"523_CR33","unstructured":"Silver D, Sutton RS, M\u00fcller M (2007) Reinforcement Learning of Local Shape in the Game of Go. In IJCAI, 7: 1053\u20131058"},{"key":"523_CR34","doi-asserted-by":"crossref","unstructured":"So\u011fanl\u0131 A, Cetin M (2015) Low-rank sparse matrix decomposition for sparsity-driven SAR image reconstruction. In 2015 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa) IEEE, 239\u2013243","DOI":"10.1109\/CoSeRa.2015.7330300"},{"issue":"1","key":"523_CR35","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1109\/36.481897","volume":"34","author":"AHS Solberg","year":"1996","unstructured":"Solberg AHS, Taxt T, Jain AK (1996) A Markov random field model for classification of multisource satellite imagery. IEEE Trans Geosci Remote Sens 34(1):100\u2013113","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"523_CR36","unstructured":"Sutton RS, Barto AG (2018) Reinforcement learning: an introduction. MIT Press"},{"key":"523_CR37","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/978-94-009-9394-5_18","volume-title":"Philosophy in geography","author":"WR Tobler","year":"1979","unstructured":"Tobler WR (1979) Cellular geography. Philosophy in geography, vol 20. Springer, Dordrecht, pp 379\u2013386"},{"key":"523_CR38","doi-asserted-by":"crossref","unstructured":"Veena KM, Manjula SK, Ajitha SKB (2018) Performance comparison of machine learning classification algorithms\/\/Advances in Computing and Data Sciences: Second International Conference, ICACDS 2018, Dehradun, India, April 20\u201321, 2018, Revised Selected Papers, Part II 2 Springer Singapore, 489\u2013497","DOI":"10.1007\/978-981-13-1813-9_49"},{"issue":"1","key":"523_CR39","doi-asserted-by":"publisher","first-page":"4737","DOI":"10.1038\/s41598-023-32027-3","volume":"13","author":"AM Vincent","year":"2023","unstructured":"Vincent AM, Jidesh P (2023) An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms. Sci Rep 13(1):4737","journal-title":"Sci Rep"},{"key":"523_CR40","doi-asserted-by":"crossref","unstructured":"Wang X, Shangguan H, Huang F et al (2024) MEL: efficient multi-task evolutionary learning for high-dimensional feature selection. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2024.3366333"},{"key":"523_CR41","doi-asserted-by":"crossref","unstructured":"Watkins CJCH, Dayan P (1992) Q-learning. Machine learning, 1992, 8(3): 279\u2013292","DOI":"10.1023\/A:1022676722315"},{"key":"523_CR42","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.neunet.2021.12.010","volume":"148","author":"D Wen","year":"2022","unstructured":"Wen D et al (2022) Multi-dimensional conditional mutual information with application on the EEG signal analysis for spatial cognitive ability evaluation. Neural Netw 148:23\u201336. https:\/\/doi.org\/10.1016\/j.neunet.2021.12.010","journal-title":"Neural Netw"},{"issue":"10","key":"523_CR43","doi-asserted-by":"publisher","first-page":"2196","DOI":"10.1109\/TGRS.2002.802473","volume":"40","author":"H Xie","year":"2002","unstructured":"Xie H, Pierce LE, Ulaby FT (2002) SAR speckle reduction using wavelet denoising and Markov random field modeling. IEEE Trans Geosci Remote Sens 40(10):2196\u20132212","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"3","key":"523_CR44","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1111\/tgis.12547","volume":"23","author":"B Yan","year":"2019","unstructured":"Yan B et al (2019) A spatially explicit reinforcement learning model for geographic knowledge graph summarization. Trans GIS 23(3):620\u2013640. https:\/\/doi.org\/10.1111\/tgis.12547","journal-title":"Trans GIS"},{"key":"523_CR45","first-page":"102751","volume":"108","author":"W Yu","year":"2022","unstructured":"Yu W, Chen J, Wei C (2022) A hierarchical learning model for inferring the labels of points of interest with unbalanced data distribution. Int J Appl Earth Obs Geoinf 108:102751","journal-title":"Int J Appl Earth Obs Geoinf"},{"issue":"5","key":"523_CR46","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1111\/tgis.12679","volume":"24","author":"G Zhang","year":"2020","unstructured":"Zhang G, Zhu AX (2020) Sample size and spatial configuration of volunteered geographic information affect effectiveness of spatial bias mitigation. Trans GIS 24(5):1315\u20131340","journal-title":"Trans GIS"},{"issue":"5","key":"523_CR47","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1109\/LGRS.2018.2809905","volume":"15","author":"J Zhang","year":"2018","unstructured":"Zhang J et al (2018) DEM generation using circular SAR data based on low-rank and sparse matrix decomposition. IEEE Geosci Remote Sens Lett 15(5):724\u2013728","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"523_CR48","doi-asserted-by":"crossref","unstructured":"Zhu Y et al (2017) Target-driven visual navigation in indoor scenes using deep reinforcement learning. In 2017 IEEE international conference on robotics and automation (ICRA). IEEE, 3357\u20133364","DOI":"10.1109\/ICRA.2017.7989381"}],"container-title":["GeoInformatica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-024-00523-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10707-024-00523-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-024-00523-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T09:19:23Z","timestamp":1739870363000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10707-024-00523-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,17]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["523"],"URL":"https:\/\/doi.org\/10.1007\/s10707-024-00523-x","relation":{},"ISSN":["1384-6175","1573-7624"],"issn-type":[{"value":"1384-6175","type":"print"},{"value":"1573-7624","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,17]]},"assertion":[{"value":"17 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}