Recently, hierarchical text classification has become an active research topic. The essential idea is that the descendant classes can share the information of the ancestor classes in a predefined taxonomy. In this paper, we claim that each class has several latent concepts and its subclasses share information with these different concepts respectively. Then, we propose a variant Passive-Aggress...

In a hierarchical categorization problem, categories are partially ordered to form a hierarchy. In this dissertation, we explore two main aspects of hierarchical categorization: learning algorithms and performance evaluation. We introduce the notion of consistent hierarchical classification that makes classification results more comprehensible and easily interpretable for end-users. Among the p...

This paper addresses the classification task of data mining (a form of supervised learning) in the context of an important bioinformatics problem, namely the prediction of protein functions. This problem is cast as a hierarchical classification problem. The protein functions to be predicted correspond to classes that are arranged in a hierarchical structure (this takes the form of a class tree)...

Hierarchical models have been shown to be effective in content classification. However, the performance of the model heavily depends on the given hierarchical taxonomy. We empirically show that different taxonomies can result in significant differences in hierarchical classification performance. Motivated by some real application problems, we aim to modify a content taxonomy automatically for d...

The problem of automatic classification of seven types of eating conditions from speech is considered. Based on the confusion among different eating conditions from a seven class support vector machine (SVM) classifier, a hierarchical SVM classifier is designed. Experiments on the iHEARu-EAT database show that the hierarchical classifier results in a better classification accuracy compared to a...

In the domain of many relevant classification problems, classes are organized in hierarchies, representing specialization relationships between them. These are the so-called hierarchical classification problems. Methods based on different approaches have been used to solve them, trying to achieve better predictive performance. In this work, we propose two local per level hierarchical classifier...

This paper addresses the classification task of data mining (a form of supervised learning) in the context of an important bioinformatics problem, namely the prediction of protein functions. This problem is cast as a hierarchical classification problem, where the protein functions to be predicted correspond to classes that are arranged in a hierarchical structure, in the form of a class tree. T...

This paper describes a hierarchical system that predicts one label at a time for automated student response analysis. For the task, we build a classification binary tree that delays more easily confused labels to later stages using hierarchical processes. In particular, the paper describes how the hierarchical classifier has been built and how the classification task has been broken down into b...

Abstract This study aimed to present the South African maize industry with an accurate and affordable automated analytical technique for white grading using near infrared (NIR) spectral imaging. The 17 categories sub-categories stipulated in legislation were simultaneously classified (1044 samples; 60 kernels of each class) 25 partial least squares discriminant analysis (PLS-DA) models. models ...

This paper focuses on hierarchical classification problems where the classes to be predicted are organized in the form of a tree. The standard top-down divide and conquer approach for hierarchical classification consists of building a hierarchy of classifiers where a classifier is built for each internal (non-leaf) node in the class tree. Each classifier discriminates only between its child cla...