scholarly journals A Hierarchical Classification of Farm Systems

1988 ◽  
Vol 24 (4) ◽  
pp. 399-419 ◽  
Author(s):  
L. O. Fresco ◽  
E. Westphal

SummaryA framework is proposed for the classification of farm systems, which are defined as decisionmaking units comprising farm household, cropping and livestock systems that transform land, capital and labour into products for consumption and sale. Two general principles underlying the classification are outlined. First, since farm systems are embedded in a hierarchical structure, the classification is based on the characteristics of the underlying systems and their interactions. Secondly, ecological factors, i.e. physical and biological parameters, are the primary determinants of farm systems. Changes in farm systems, at least in the foreseeable future, depend on the development of socio-economic variables. The classification is summarized in a set of comprehensive tables.L. O. Fresco y E. Westphal: Una clasificación jerárquica de sistemas agrícolas

AI Magazine ◽  
2019 ◽  
Vol 40 (2) ◽  
pp. 59-65
Author(s):  
Denali Molitor ◽  
Deanna Needell

In classification problems, especially those that categorize data into a large number of classes, the classes often naturally follow a hierarchical structure. That is, some classes are likely to share similar structures and features. Those characteristics can be captured by considering a hierarchical relationship among the class labels. Motivated by a recent simple classification approach on binary data, we propose a variant that is tailored to efficient classification of hierarchical data. In certain settings, specifically, when some classes are significantly easier to identify than others, we show case computational and accuracy advantages.


Author(s):  
A. K. Cherkashin ◽  

A hierarchical system is the result of dividing a set of objects into subordinate groups in order from highest to lowest, where each lower level reveals and clarifies the properties of objects at a higher level. There is a difference between the natural hierarchy of geosystems-geochors and the hierarchy of geomers, which leads to taxonomic classification. Theoretical basis for creating a hierarchical classification of geosystems are developed using a conceptual model of geographical cycles of accumulation and removal of factor load on territorial objects of various scales. The cone of chorological and typological connections is considered as the basic metamodel of hierarchical structure. For its research, we use descriptive geometry tools to represent the cone in the vertical and horizontal (plan) projections. The surface and unfolding structures of the cone with sections at different levels reflect the hierarchy. The planned projection in the form of concentric structures is considered as model of the archetype of hierarchy formation. The horological and typological classifications converge in the position “natural zone” as the “parent core” of the type of natural environment, which represents the zonal norm. The concentric model has various interpretations, in particular, it is described as a system of local coordinates, where each coordinate corresponds to the categories of seriality of geosystems, i.e. the degree of their factoral-dynamic variability relatively to zonal geosystems. In the coordinate approach, the classification looks like a ranked set of merons and taxa, where the meron categories are represented by quantum numbers of the coordinate series, and the taxon is a sequence of such numbers of different series (numeric code). The formation of hierarchical classification is based on the triad principle, when the taxon of the upper level is divided into three lower level gradations, which are arranged in a homological series according to the degree of seriality. There is an analogy between the hierarchical structure of the periodic system of chemical elements and the typological classification of geosystems, when the periods of the system of elements correspond to the high-altitude layers and latitudinal zones of geochor placement or hierarchical levels of geomer classification. An unfolding and plan projection of the classification cone of facies for the Prichunsky landscape of the southern taiga of Central Siberia in three basic categories of variability of different levels geomers are presented.


Sensors ◽  
2014 ◽  
Vol 14 (6) ◽  
pp. 11204-11224 ◽  
Author(s):  
Atena Fekr ◽  
Majid Janidarmian ◽  
Katarzyna Radecka ◽  
Zeljko Zilic

2008 ◽  
Vol 1 (1) ◽  
pp. 67 ◽  
Author(s):  
Matthew N Davies ◽  
Andrew Secker ◽  
Mark Halling-Brown ◽  
David S Moss ◽  
Alex A Freitas ◽  
...  

2021 ◽  
Author(s):  
Rajan Saha Raju ◽  
Abdullah Al Nahid ◽  
Preonath Shuvo ◽  
Rashedul Islam

AbstractTaxonomic classification of viruses is a multi-class hierarchical classification problem, as taxonomic ranks (e.g., order, family and genus) of viruses are hierarchically structured and have multiple classes in each rank. Classification of biological sequences which are hierarchically structured with multiple classes is challenging. Here we developed a machine learning architecture, VirusTaxo, using a multi-class hierarchical classification by k-mer enrichment. VirusTaxo classifies DNA and RNA viruses to their taxonomic ranks using genome sequence. To assign taxonomic ranks, VirusTaxo extracts k-mers from genome sequence and creates bag-of-k-mers for each class in a rank. VirusTaxo uses a top-down hierarchical classification approach and accurately assigns the order, family and genus of a virus from the genome sequence. The average accuracies of VirusTaxo for DNA viruses are 99% (order), 98% (family) and 95% (genus) and for RNA viruses 97% (order), 96% (family) and 82% (genus). VirusTaxo can be used to detect taxonomy of novel viruses using full length genome or contig sequences.AvailabilityOnline version of VirusTaxo is available at https://omics-lab.com/virustaxo/.


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