Network-based high level data classification


Autoria(s): Silva, Thiago Christiano; Liang, Zhao
Contribuinte(s)

UNIVERSIDADE DE SÃO PAULO

Data(s)

07/11/2013

07/11/2013

2012

Resumo

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

Sao Paulo State Research Foundation (FAPESP)

Brazilian National Research Council (CNPq)

Identificador

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, PISCATAWAY, v. 23, n. 6, p. 954-970, JUN, 2012

2162-237X

http://www.producao.usp.br/handle/BDPI/43195

10.1109/TNNLS.2012.2195027

http://dx.doi.org/10.1109/TNNLS.2012.2195027

Idioma(s)

eng

Publicador

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

PISCATAWAY

Relação

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

Direitos

restrictedAccess

Copyright IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Palavras-Chave #COMPLEX NETWORKS #CONTEXTUAL CLASSIFIER #HIGH LEVEL CLASSIFICATION #SUPERVISED LEARNING #SUPPORT VECTOR MACHINES #COMPLEX NETWORKS #SEMANTIC WEB #NEURAL-NETWORK #COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE #COMPUTER SCIENCE, HARDWARE & ARCHITECTURE #COMPUTER SCIENCE, THEORY & METHODS #ENGINEERING, ELECTRICAL & ELECTRONIC
Tipo

article

original article

publishedVersion