Hierarchical text classification (HTC) is essential for various real applications. However, HTC models are challenging to develop because they often require processing a large volume of documents and labels with hierarchical taxonomy. Recent based on deep learning have attempted incorporate hierarchy information into model structure. Consequently, these implement when the parameters increase la...

Remote sensing scene classification deals with the problem of classifying land use/cover a region from images. To predict development and socioeconomic structures cities, status use in regions is tracked by national mapping agencies countries. Many these land-use types that are arranged multiple levels. In this paper, we examined efficiency hierarchically designed convolutional neural network (...

Many of the works of text classification involve the attribution of each text a single class label from a predefined set of classes, usually small and flat organized (flat classification). However, there are more complex classification problems in which we can assign to each text more than one class (multi-label classification), that can be organized in a hierarchical structure (hierarchical cl...

Hierarchical text classification or simply hierarchical classification refers to assigning a document to one or more suitable categories from a hierarchical category space. In our literature survey, we have found that the existing hierarchical classification experiments used a variety of measures to evaluate performance. These performance measures often assume independence between categories an...

The objective of the paper is to explore classification on magnetic resonance images (MRI). In our work on MRI classification, two types of classification (flat and hierarchical) are addressed and explored. The examination is conducted on the dataset of magnetic resonance images that have hierarchical organization. All images are described by using Edge histogram descriptor for the feature extr...

This chapter proposes a novel hierarchical classification system based on the K-Nearest Neighbors (K-NN) model and its application to nonmelanoma skin lesion classification. Color and texture features are extracted from skin lesion images. The hierarchical structure decomposes the classification task into a set of simpler problems, one at each node of the classification. Feature selection is em...

This paper proposes a novel Ant Colony Optimisation algorithm (ACO) tailored for the hierarchical multilabel classification problem of protein function prediction. This problem is a very active research field, given the large increase in the number of uncharacterised proteins available for analysis and the importance of determining their functions in order to improve the current biological know...