Optimizing feature extraction for multiclass problems

E. Choi, C. Lee

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Feature extraction has been an important research topic in pattern classification and has been studied extensively by many researchers. Most of the conventional feature extraction methods are performed using a criterion function defined between two classes or a global function. Although these methods work relatively well in most cases, it is generally not optimal in any sense for multiclass problems. In order to address this problem, we propose a method to optimize feature extraction for multiclass problems. We first investigated the distribution of classification accuracies of multiclass problems in the feature space and found that there exist much better feature sets that the conventional feature extraction algorithms fail to find. Then we proposed an algorithm that finds such features. Experiments with remotely sensed data show that the proposed algorithm consistently provides better performances compared with the conventional feature extraction algorithms.

Original languageEnglish
Pages (from-to)521-528
Number of pages8
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume39
Issue number3
DOIs
Publication statusPublished - 2001 Mar 1

Fingerprint

pattern recognition
Feature extraction
extraction method
Pattern recognition
experiment
Experiments
method

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

Cite this

@article{26c03e16fdec4f43b4d07d50196fc54d,
title = "Optimizing feature extraction for multiclass problems",
abstract = "Feature extraction has been an important research topic in pattern classification and has been studied extensively by many researchers. Most of the conventional feature extraction methods are performed using a criterion function defined between two classes or a global function. Although these methods work relatively well in most cases, it is generally not optimal in any sense for multiclass problems. In order to address this problem, we propose a method to optimize feature extraction for multiclass problems. We first investigated the distribution of classification accuracies of multiclass problems in the feature space and found that there exist much better feature sets that the conventional feature extraction algorithms fail to find. Then we proposed an algorithm that finds such features. Experiments with remotely sensed data show that the proposed algorithm consistently provides better performances compared with the conventional feature extraction algorithms.",
author = "E. Choi and C. Lee",
year = "2001",
month = "3",
day = "1",
doi = "10.1109/36.911110",
language = "English",
volume = "39",
pages = "521--528",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

Optimizing feature extraction for multiclass problems. / Choi, E.; Lee, C.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 3, 01.03.2001, p. 521-528.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Optimizing feature extraction for multiclass problems

AU - Choi, E.

AU - Lee, C.

PY - 2001/3/1

Y1 - 2001/3/1

N2 - Feature extraction has been an important research topic in pattern classification and has been studied extensively by many researchers. Most of the conventional feature extraction methods are performed using a criterion function defined between two classes or a global function. Although these methods work relatively well in most cases, it is generally not optimal in any sense for multiclass problems. In order to address this problem, we propose a method to optimize feature extraction for multiclass problems. We first investigated the distribution of classification accuracies of multiclass problems in the feature space and found that there exist much better feature sets that the conventional feature extraction algorithms fail to find. Then we proposed an algorithm that finds such features. Experiments with remotely sensed data show that the proposed algorithm consistently provides better performances compared with the conventional feature extraction algorithms.

AB - Feature extraction has been an important research topic in pattern classification and has been studied extensively by many researchers. Most of the conventional feature extraction methods are performed using a criterion function defined between two classes or a global function. Although these methods work relatively well in most cases, it is generally not optimal in any sense for multiclass problems. In order to address this problem, we propose a method to optimize feature extraction for multiclass problems. We first investigated the distribution of classification accuracies of multiclass problems in the feature space and found that there exist much better feature sets that the conventional feature extraction algorithms fail to find. Then we proposed an algorithm that finds such features. Experiments with remotely sensed data show that the proposed algorithm consistently provides better performances compared with the conventional feature extraction algorithms.

UR - http://www.scopus.com/inward/record.url?scp=0035275083&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035275083&partnerID=8YFLogxK

U2 - 10.1109/36.911110

DO - 10.1109/36.911110

M3 - Article

AN - SCOPUS:0035275083

VL - 39

SP - 521

EP - 528

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

SN - 0196-2892

IS - 3

ER -