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
summary:First some new concepts are introduced (extension of the similarity-function, similarity-mea...
Decision Tree classifier builds a classification model using training data. It consists of records h...
Research in the field of supervised classification has mostly focused on the standard, so-called “fl...
Abstract. In the domain of many classification problems, classes have relations of dependency that a...
Several popular Machine Learning techniques are originally designed for the solution of two-class pr...
In the domain of many classification problems, classes have relations of dependency that are represe...
This electronic version was submitted by the student author. The certified thesis is available in th...
International audienceWe describe a new approach for dealing with hierarchical classification with a...
Machine learning techniques have been very efficient in many applications, in particular, when learn...
Brown, Steven D.Classification spans a broad range of chemical analysis tasks. The majority of curre...
Traditional flat classification methods (e.g., binary, multiclass, and multi-label classification) s...
Hierarchical classification is a challenging problem where the class labels are organized in a prede...
Classification problems in remote sensing are often difficult because of high dimensionality of the ...
Trees are a common way of organizing large amounts of information by placing items with similar ch...
The main problem considered in this paper consists of binarizing categorical (nominal) attributes ha...
summary:First some new concepts are introduced (extension of the similarity-function, similarity-mea...
Decision Tree classifier builds a classification model using training data. It consists of records h...
Research in the field of supervised classification has mostly focused on the standard, so-called “fl...
Abstract. In the domain of many classification problems, classes have relations of dependency that a...
Several popular Machine Learning techniques are originally designed for the solution of two-class pr...
In the domain of many classification problems, classes have relations of dependency that are represe...
This electronic version was submitted by the student author. The certified thesis is available in th...
International audienceWe describe a new approach for dealing with hierarchical classification with a...
Machine learning techniques have been very efficient in many applications, in particular, when learn...
Brown, Steven D.Classification spans a broad range of chemical analysis tasks. The majority of curre...
Traditional flat classification methods (e.g., binary, multiclass, and multi-label classification) s...
Hierarchical classification is a challenging problem where the class labels are organized in a prede...
Classification problems in remote sensing are often difficult because of high dimensionality of the ...
Trees are a common way of organizing large amounts of information by placing items with similar ch...
The main problem considered in this paper consists of binarizing categorical (nominal) attributes ha...
summary:First some new concepts are introduced (extension of the similarity-function, similarity-mea...
Decision Tree classifier builds a classification model using training data. It consists of records h...
Research in the field of supervised classification has mostly focused on the standard, so-called “fl...