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SUSI: Supervised Self-Organizing Maps for Regression and Classification in Python

Riese, Felix M.; Keller, Sina

In many research fields, the sizes of the existing datasets vary widely. Hence, there is a need for machine learning techniques which are well-suited for these different datasets. One possible technique is the self-organizing map (SOM), a type of artificial neural network which is, so far, weakly represented in the field of machine learning. The SOM’s unique characteristic is the neighborhood relationship of the output neurons. This relationship improves the ability of generalization on small datasets. SOMs are mostly applied in unsupervised learning and few studies focus on using SOMs as supervised learning approach. Furthermore, no appropriate SOM package is available with respect to machine learning standards and in the widely used programming language Python. In this paper, we introduce the freely available SUpervised Self-organIzing maps (SUSI) Python package which performs supervised regression and classification. The implementation of SUSI is described with respect to the underlying mathematics. Then, we present first evaluations of the SOM for regression and classification datasets from two different domains of geospatial image analysis. ... mehr

Zugehörige Institution(en) am KIT Fakultät für Bauingenieur-, Geo- und Umweltwissenschaften (BGU)
Institut für Photogrammetrie und Fernerkundung (IPF)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Forschungsbericht/Preprint
Publikationsjahr 2019
Sprache Englisch
Identifikator KITopen-ID: 1000092723
Projektinformation TRUST; GROW-TP2 (BMBF, EU 6. RP, 02WGR1426B)
Vorab online veröffentlicht am 28.03.2019
Externe Relationen Abstract/Volltext
Schlagwörter Self-Organizing Maps Machine Learning Unsupervised Learning Supervised Learning Python
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