A1 Journal article (refereed)
Semantics of Voids within Data : Ignorance-Aware Machine Learning (2021)


Terziyan, V., & Nikulin, A. (2021). Semantics of Voids within Data : Ignorance-Aware Machine Learning. Isprs International Journal of Geo-Information, 10(4), Article 246. https://doi.org/10.3390/ijgi10040246


JYU authors or editors


Publication details

All authors or editors: Terziyan, Vagan; Nikulin, Anton

Journal or series: Isprs International Journal of Geo-Information

eISSN: 2220-9964

Publication year: 2021

Publication date: 08/04/2021

Volume: 10

Issue number: 4

Article number: 246

Publisher: MDPI AG

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.3390/ijgi10040246

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/75078


Abstract

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.


Keywords: data mining; geographic information; classification; machine learning

Free keywords: data semantics; data mining; classification; ignorance; data voids; prototype selection; adversarial learning


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2021

JUFO rating: 1


Last updated on 2024-03-04 at 20:15