Optimizing feature extraction for multiclass cases

Chulhee Lee, Joonyong Hong

Research output: Contribution to journalConference article

3 Citations (Scopus)

Abstract

In this paper, we propose a new optimization method for multiclass feature extraction problems by assigning weights to each class in computing the global criterion function and adjusting the weights as new features are extracted. Recently, it is shown that it is possible to predict the classification error within 1-2% margin from the Bhattacharyya distance. We use the error prediction technique to adjust the weights of each classes. Initially, we assign equal weights to each class. After the first feature is extracted, we calculate classification error of each class when the first feature is used and adjust the weights accordingly. We compute again the global criterion function with a new set of weights excluding the first feature and calculate the second feature from the revised criterion function, and so on. Preliminary experiments show improvement over the conventional methods.

Original languageEnglish
Pages (from-to)2545-2548
Number of pages4
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume3
Publication statusPublished - 1997 Dec 1

Fingerprint

Feature extraction
Experiments

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Control and Systems Engineering

Cite this

@article{cac252a8bad44ca39de347de01367c1f,
title = "Optimizing feature extraction for multiclass cases",
abstract = "In this paper, we propose a new optimization method for multiclass feature extraction problems by assigning weights to each class in computing the global criterion function and adjusting the weights as new features are extracted. Recently, it is shown that it is possible to predict the classification error within 1-2{\%} margin from the Bhattacharyya distance. We use the error prediction technique to adjust the weights of each classes. Initially, we assign equal weights to each class. After the first feature is extracted, we calculate classification error of each class when the first feature is used and adjust the weights accordingly. We compute again the global criterion function with a new set of weights excluding the first feature and calculate the second feature from the revised criterion function, and so on. Preliminary experiments show improvement over the conventional methods.",
author = "Chulhee Lee and Joonyong Hong",
year = "1997",
month = "12",
day = "1",
language = "English",
volume = "3",
pages = "2545--2548",
journal = "Proceedings of the IEEE International Conference on Systems, Man and Cybernetics",
issn = "0884-3627",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

Optimizing feature extraction for multiclass cases. / Lee, Chulhee; Hong, Joonyong.

In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Vol. 3, 01.12.1997, p. 2545-2548.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Optimizing feature extraction for multiclass cases

AU - Lee, Chulhee

AU - Hong, Joonyong

PY - 1997/12/1

Y1 - 1997/12/1

N2 - In this paper, we propose a new optimization method for multiclass feature extraction problems by assigning weights to each class in computing the global criterion function and adjusting the weights as new features are extracted. Recently, it is shown that it is possible to predict the classification error within 1-2% margin from the Bhattacharyya distance. We use the error prediction technique to adjust the weights of each classes. Initially, we assign equal weights to each class. After the first feature is extracted, we calculate classification error of each class when the first feature is used and adjust the weights accordingly. We compute again the global criterion function with a new set of weights excluding the first feature and calculate the second feature from the revised criterion function, and so on. Preliminary experiments show improvement over the conventional methods.

AB - In this paper, we propose a new optimization method for multiclass feature extraction problems by assigning weights to each class in computing the global criterion function and adjusting the weights as new features are extracted. Recently, it is shown that it is possible to predict the classification error within 1-2% margin from the Bhattacharyya distance. We use the error prediction technique to adjust the weights of each classes. Initially, we assign equal weights to each class. After the first feature is extracted, we calculate classification error of each class when the first feature is used and adjust the weights accordingly. We compute again the global criterion function with a new set of weights excluding the first feature and calculate the second feature from the revised criterion function, and so on. Preliminary experiments show improvement over the conventional methods.

UR - http://www.scopus.com/inward/record.url?scp=0031354601&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031354601&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0031354601

VL - 3

SP - 2545

EP - 2548

JO - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

JF - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

SN - 0884-3627

ER -