scholarly journals Hierarchical Classification Using Binary Data

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):  
Alex Freitas ◽  
André C.P.L.F. de Carvalho

In machine learning and data mining, most of the works in classification problems deal with flat classification, where each instance is classified in one of a set of possible classes and there is no hierarchical relationship between the classes. There are, however, more complex classification problems where the classes to be predicted are hierarchically related. This chapter presents a tutorial on the hierarchical classification techniques found in the literature. We also discuss how hierarchical classification techniques have been applied to the area of bioinformatics (particularly the prediction of protein function), where hierarchical classification problems are often found.


2009 ◽  
Vol 13 (2) ◽  
pp. 43 ◽  
Author(s):  
Iwin Leenen ◽  
Eva Ceulemans

Hierarchical classes (HICLAS) models for multi-way multi-mode data constitute a unique family of classification models in that (a) they simultaneously induce a hierarchical classification (of the elements) of each mode and (b) they link the hierarchical classifications together by an association relation that yieldsa predicted (or reconstructed) value for each cell in the data array. For the case of three-way three-mode binary data, the most prominent HICLAS models include INDCLAS and Tucker3-HICLAS. In this paper, we compare the latter two models, introducing the underlying theory of both in substantive terms and showing how a Tucker3-HICLAS analysis may result in a simpler model than that yielded by INDCLAS, although the former is mathematically more complex than the latter (which it includes as a special case). We illustrate by two applications: astudy on anger responses in frustrating situations and a case-study on emotions in interpersonal relations.


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


Author(s):  
Alex Freitas ◽  
André Carvalho

In machine learning and data mining, most of the works in classification problems deal with flat classification, where each instance is classified in one of a set of possible classes and there is no hierarchical relationship between the classes. There are, however, more complex classification problems where the classes to be predicted are hierarchically related. This chapter presents a tutorial on the hierarchical classification techniques found in the literature. We also discuss how hierarchical classification techniques have been applied to the area of bioinformatics (particularly the prediction of protein function), where hierarchical classification problems are often found.


2008 ◽  
pp. 119-145
Author(s):  
Alex Freitas ◽  
Andre´ C.P.L.F. de Carvalho

In machine learning and data mining, most of the works in classification problems deal with flat classification, where each instance is classified in one of a set of possible classes and there is no hierarchical relationship between the classes. There are, however, more complex classification problems where the classes to be predicted are hierarchically related. This chapter presents a tutorial on the hierarchical classification techniques found in the literature. We also discuss how hierarchical classification techniques have been applied to the area of bioinformatics (particularly the prediction of protein function), where hierarchical classification problems are often found.


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.


Author(s):  
Fadime Üney Yüksektepe

Data classification is a supervised learning strategy that analyzes the organization and categorization of data in distinct classes. Generally, a training set, in which all objects are already associated with known class labels, is used in classification methods. The data classification algorithms work on this set by using input attributes and builds a model to classify new objects. In other words, the algorithm predicts output attribute values. Output attribute of the developed model is categorical (Roiger & Geatz, 2003). There are many applications of data classification in finance, health care, sports, engineering and science. Data classification is an important problem that has applications in a diverse set of areas ranging from finance to bioinformatics (Chen & Han & Yu, 1996; Edelstein, 2003; Jagota, 2000). Majority data classification methods are developed for classifying data into two groups. As multi-group data classification problems are very common but not widely studied, we focus on developing a new multi-group data classification approach based on mixed-integer linear programming.


2012 ◽  
Vol 6-7 ◽  
pp. 742-747
Author(s):  
Yun Bo Xiong

There always exists semantic hierarchical relationship in text classification. Therefore, it's inevitable to organize documents in accordance with the hierarchical structure. Based on confusion matrix, this paper attempted to adopt two different algorithms including hierarchical clustering and confusion category to build hierarchical structure of document category, and finally made use of hierarchical classification to carry on experiment, results of which showed that the confusion category strategy is superior to hierarchical clustering strategy and recall and precision of flat classification are both improved.


2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.


2021 ◽  
Vol 11 (9) ◽  
pp. 3974
Author(s):  
Laila Bashmal ◽  
Yakoub Bazi ◽  
Mohamad Mahmoud Al Rahhal ◽  
Haikel Alhichri ◽  
Naif Al Ajlan

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.


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