Association rules enhanced classification of underwater acoustic signal

Author

Chen, Jie ; Li, Haiying ; Tang, Shiwei

Author_Institution

Nat. Lab. on Machine Perception, Peking Univ., China

fYear

2001

fDate

2001

Firstpage

582

Lastpage

583

Abstract

The classification of underwater acoustic signals is one of the important fields of pattern recognition. Inspired by the experience of training human experts in sonar, we propose a two-phase training algorithm to exploit association rules to reveal understandable intrinsic rules which contribute to correct classification in known mis-classification data sets. Preliminary experimental results demonstrate the potential of these classification association rules to enhance the classification accuracy of underwater acoustic signals

Keywords

data mining; learning (artificial intelligence); signal classification; sonar signal processing; underwater sound; classification accuracy enhancement; classification association rules; misclassification data sets; pattern recognition; sonar; two-phase training algorithm; understandable intrinsic rules; underwater acoustic signal classification; Association rules; Classification algorithms; Classification tree analysis; Data mining; Feature extraction; Laboratories; Neural networks; Pattern recognition; Sonar; Underwater acoustics;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on

Conference_Location

San Jose, CA

Print_ISBN

0-7695-1119-8

Type

conf

DOI

10.1109/ICDM.2001.989569

Filename

989569