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There are three main limitations. First, the graphs used in these approaches are usually predefined regardless of the task at hand. Second, due to the use of graphs, almost all approaches are unable to process and consider data with a very large number of unlabeled samples. Thirdly, the imbalance of the topology of the samples is very often not taken into account. In particular, processing large datasets with GSSL might pose challenges in terms of computational resource feasibility. In this article, we present a scalable and inductive GSSL method. We broaden the scope of the graph topology imbalance paradigm to extensive databases. Second, we employ the calculated weights of the labeled sample for the label-matching term in the global objective function. This leads to a unified, scalable, semi-supervised learning model that allows simultaneous labeling of unlabeled data, projection of the feature space onto the labeling space, along with the graph matrix of anchors. In the proposed scheme, the integration of labels and features from anchors is applied for the adaptive construction of the anchor graph. Experimental results were performed on four large databases: NORB, RCV1, Covtype, and MNIST. These experiments demonstrate that the proposed method exhibits superior performance when compared to existing scalable semi-supervised learning models.<\/jats:p>","DOI":"10.1145\/3643645","type":"journal-article","created":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T12:15:55Z","timestamp":1706616955000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Scalable and Inductive Semi-supervised Classifier with Sample Weighting Based on Graph Topology"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6581-9680","authenticated-orcid":false,"given":"Fadi","family":"Dornaika","sequence":"first","affiliation":[{"name":"University of the Basque Country UPV\/EHU, San Sebastian, Spain and IKERBASQUE, Basque Foundation for Science, Bilbao, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6272-4946","authenticated-orcid":false,"given":"Zoulfikar","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"University of the Basque Country UPV\/EHU, San Sebastian, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0372-6144","authenticated-orcid":false,"given":"Alirezah","family":"Bosaghzadeh","sequence":"additional","affiliation":[{"name":"Shahid Rajaee Teacher Training University, Tehran, Iran and Ho Chi Minh City Open University, Ho Chi Min City, Vietnam"}]}],"member":"320","published-online":{"date-parts":[[2024,3,26]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.08.127"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICESC51422.2021.9532994"},{"key":"e_1_3_2_4_2","first-page":"2399","article-title":"Manifold regularization: A geometric framework for learning from labeled and unlabeled examples","author":"Belkin Mikhail","year":"2006","unstructured":"Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani. 2006. 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