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Semantics of Voids within Data: Ignorance-Aware Machine Learning

Faculty of Information Technology, University of Jyväskylä, P.O. Box-35, FI-40014 Jyväskylä, Finland
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Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(4), 246; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040246
Received: 16 December 2020 / Revised: 7 February 2021 / Accepted: 5 April 2021 / Published: 8 April 2021
(This article belongs to the Special Issue Geospatial Semantic Web: Resources, Tools and Applications)
Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, known, and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples that are not yet observed, known, or understood. These tools traditionally take semantically labeled samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach, and we suggest considering the things the other way around. What if the task would be as follows: how to build a model based on the semantics of our ignorance, i.e., by processing the shape of “voids” within the available data space? Can we improve traditional classification by also modeling the ignorance? In this paper, we provide some algorithms for the discovery and visualization of the ignorance zones in two-dimensional data spaces and design two ignorance-aware smart prototype selection techniques (incremental and adversarial) to improve the performance of the nearest neighbor classifiers. We present experiments with artificial and real datasets to test the concept of the usefulness of ignorance semantics discovery. View Full-Text
Keywords: data semantics; data mining; classification; ignorance; data voids; prototype selection; adversarial learning data semantics; data mining; classification; ignorance; data voids; prototype selection; adversarial learning
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MDPI and ACS Style

Terziyan, V.; Nikulin, A. Semantics of Voids within Data: Ignorance-Aware Machine Learning. ISPRS Int. J. Geo-Inf. 2021, 10, 246. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040246

AMA Style

Terziyan V, Nikulin A. Semantics of Voids within Data: Ignorance-Aware Machine Learning. ISPRS International Journal of Geo-Information. 2021; 10(4):246. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040246

Chicago/Turabian Style

Terziyan, Vagan; Nikulin, Anton. 2021. "Semantics of Voids within Data: Ignorance-Aware Machine Learning" ISPRS Int. J. Geo-Inf. 10, no. 4: 246. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040246

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