Hierarchical quantum classifiers

Hierarchical Rough Classifiers

The major applications of rough set theory in data mining are related to the modeling of concepts using rough classifiers, i.e., the algorithms classifying unseen objects into lower or upper approximations of concepts. This paper investigates a class of compound classifiers called multi-level (or hierarchical) rough classifiers (MLRC). We present the most recent issues on the construction of su...

Building Hierarchical Classifiers Using Class Proximity

In this paper, we address the need to automatically classify text documents into topic hierarchies like those in ACM Digital Library and Yahoo!. The existing local approach constructs a classi er at each split of the topic hierarchy. However, the local approach does not address the closeness of classi cation in hierarchical classi cation where the concern often is how close a classi cation is, ...

Hierarchical Classifiers Approach for Emotions Recognition

The recognition of the emotional states of speaker is a multi-disciplinary research area that has received great interest in the last years. One of the more important goals is to improve the voiced-based human-machine interactions. Recent works on this domain use the prosodic features and the spectrum characteristics of speech signal, with standard classifier methods. However, there is no analy...

Learning Hierarchical Classifiers with Class Taxonomies

As more and more data with class taxonomies emerge in diverse fields, such as pattern recognition, text classification and gene function prediction, we need to extend traditional machine learning methods to solve classification problem in such data sets, which presents more challenges over common pattern classification problems. In this paper, we define structured label classification problem a...

An ALgorithm for Learning Hierarchical Classifiers