Research Article
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Kutuplaştırılmış veri üzerinde ikili sınıflandırma için sürekli zamanlı eşik değeri belirleme

Year 2019, Volume: 25 Issue: 5, 596 - 602, 21.10.2019

Abstract

İkili
sınıflandırma, veri elemanlarından bir kısmını belirli karakteristiklerine göre
diğerlerinden anlamlı bir şekilde ayırmak için kullanılmaktadır. Denetimli
sınıflandırma teknikleri ise genellikle veriden çıkarılacak elemanların ayırt
edici karakteristiklerini belirlemeye yardımcı olan referans veriyi
kullanmaktadır. Bu teknikler aynı zamanda mevcut özellikleri kullanarak bütün
veri için referans veriye uygun olarak yeni özellikler oluşturmaktadır. Yeni
özellikler oluşturmanın amaçlarından birisi de çıkarılacak veri elemanlarını ve
diğerlerini ikili sınıflandırma için bir koordinat ekseni üzerinde ayrı
kutuplara doğru kutuplaştırmaktır. Bu şekilde, sadece bir eksen üzerinde eşik
değeri kullanarak, ikili sınıflandırma işlemi kolaylaşmaktadır. Bu çalışmada,
veriyi kutuplaştırmak için doğrusal ayrıştırma analizi (DAA) kullanılmış ve
bazı belirli eşik değerleriyle elde edilen ikili sınıflandırma çıktılarının
harmonik ortalama F-score değerlerini kullanan bir eşik değeri belirleme
algoritması önerilmiştir. Önerilen metottaki anahtar durum, en uygun eşik
değeri en iyi sınıflandırma başarısını (F-score değerini) vermeli ve diğer eşik
değerleri en iyi eşik değerinden uzaklaştıkça (eksenin iki ucuna doğru
ilerledikçe) daha düşük sınıflandırma başarısını vermelidir. Önerilen metot,
referans görüntüleri de içeren bir 2D anlamsal etiketleme veri kümesinden
alınan bir uzaktan algılama görüntüsü üzerinde bazı anlamlı verilerin ikili
sınıflandırması için uygulanmıştır. Önerilen metot en iyi eşik değerine sürekli
zamanlı olarak belirlenen örnekleme sayısına ve sonlanma ölçütüne göre
logaritmik zamanda yakınsamaktadır.

References

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  • Wang W, Yang N, Zhang Y, Wang F, Cao T, Eklund P. “A review of road extraction from remote sensing images”. Journal of Traffic and Transportation Engineering, 3(3), 271-282, 2016.
  • Saglam A, Baykan NA. “A satellite image classification approach by using one dimensional discriminant analysis”. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W4, 429-435, 2018.
  • Fisher RA. “The use of multiple measures in taxonomic problems”. Annals of Eugenics, 7(2), 179-188, 1936.
  • Duda RO, Hart PE, Stork DG. Pattern Classification. New York, USA, Wiley, 2000.
  • Martis RJ, Acharya UR, Min LC. “ECG beat classification using PCA, LDA, ICA and discrete wavelet transform”. Biomedical Signal Processing and Control, 8(5), 437-448, 2013.
  • Lipton ZC, Elkan C, Naryanaswamy B. “Optimal thresholding of classifiers to maximize F1 measure”. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8725(2), 225-239, 2014.
  • Sanchez IE, Belgium A, Brun M. “Optimal threshold estimation for binary classifiers using game theory”. International Society for Computational Biology Community Journal, 5(5), 1-11, 2016.
  • Weinmann M, Jutzi B, Hinz S, Mallet C. “Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers”. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 286-304, 2015.
  • Landrieu L, Raguet H, Vallet B, Mallet C, Weinmann M. “A structured regularization framework for spatially smoothing semantic labelings of 3D point clouds”. ISPRS Journal of Photogrammetry and Remote Sensing, 132, 102-118, 2017.
  • Sokolova M, Lapalme G. “A systematic analysis of performance measures for classification tasks”. Information Processing and Management, 45(4), 427-437, 2009.
  • Baldi P, Brunak S, Chauvin Y, Andersen CAF, Nielsen H. “Assessing the accuracy of prediction algorithms for classification: An overview”. Bioinformatics, 16(5), 412-424, 2000.
  • Freeman EA, Moisen GG. “A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa”. Ecological Modelling, 217(1-2), 48-58, 2008.
  • 2D Semantic Labeling Contest” “Online”. http://www2.isprs.org/commissions/comm3/wg4/semantic-labeling.html (2018).
  • Gerke M. “Use of the Stair Vision Library within the ISPRS 2D Semantic Labeling Benchmark (Vaihingen)”. Department of Earth Observation Science, University of Twente, Enschede, Netherlands, Technical Report, 2015.
  • Rottensteiner F, Sohn G, Gerke M, Baillard C, Benitez S, Breitkopf U. “ISPRS Test Project on Urban Classification and 3D Building Reconstruction”. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, I-3, 293-298, 2012.

Continuous time threshold selection for binary classification on polarized data

Year 2019, Volume: 25 Issue: 5, 596 - 602, 21.10.2019

Abstract

Binary
classification is used to distinguish some of the data elements from others in
a meaningful way according to certain characteristics.  Supervised classification techniques often
use the ground-truth data, which assists to determine the distinctive characteristics
of the elements to be extracted from the data. These techniques also generate
new features for all of the data using the current features in accordance with
the ground-truth data. One of the purposes of generating new features is to
polarize the data elements (to be extracted and others) toward the separate
pools on a coordinate axis for binary classification. In this way, the binary
classification process is easy using only a threshold value on the axis. In
this work, the Linear Discriminant Analysis (LDA) is used to polarize the data
and a threshold selection algorithm is proposed, which use the harmonic mean
F-score values of the binary classification outputs resulting from some
specific threshold values. The key condition in the proposed method is that the
most suitable threshold must give the best classification score (F-score value)
and other threshold values must give lower classification scores as they become
distant from the best threshold value (move away toward the ends of the axis).
The proposed method is experimented for binary classifications of some
meaningful elements on a remote sensing image taken from a 2D semantic
labelling dataset that has the ground-truth images. The proposed method
convergences the best threshold value continuously in logarithmic time.

References

  • Lu D, Weng Q. “A survey of image classification methods and techniques for improving classification performance”. International Journal of Remote Sensing, 28(5), 823-870, 2007.
  • Wang W, Yang N, Zhang Y, Wang F, Cao T, Eklund P. “A review of road extraction from remote sensing images”. Journal of Traffic and Transportation Engineering, 3(3), 271-282, 2016.
  • Saglam A, Baykan NA. “A satellite image classification approach by using one dimensional discriminant analysis”. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W4, 429-435, 2018.
  • Fisher RA. “The use of multiple measures in taxonomic problems”. Annals of Eugenics, 7(2), 179-188, 1936.
  • Duda RO, Hart PE, Stork DG. Pattern Classification. New York, USA, Wiley, 2000.
  • Martis RJ, Acharya UR, Min LC. “ECG beat classification using PCA, LDA, ICA and discrete wavelet transform”. Biomedical Signal Processing and Control, 8(5), 437-448, 2013.
  • Lipton ZC, Elkan C, Naryanaswamy B. “Optimal thresholding of classifiers to maximize F1 measure”. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8725(2), 225-239, 2014.
  • Sanchez IE, Belgium A, Brun M. “Optimal threshold estimation for binary classifiers using game theory”. International Society for Computational Biology Community Journal, 5(5), 1-11, 2016.
  • Weinmann M, Jutzi B, Hinz S, Mallet C. “Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers”. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 286-304, 2015.
  • Landrieu L, Raguet H, Vallet B, Mallet C, Weinmann M. “A structured regularization framework for spatially smoothing semantic labelings of 3D point clouds”. ISPRS Journal of Photogrammetry and Remote Sensing, 132, 102-118, 2017.
  • Sokolova M, Lapalme G. “A systematic analysis of performance measures for classification tasks”. Information Processing and Management, 45(4), 427-437, 2009.
  • Baldi P, Brunak S, Chauvin Y, Andersen CAF, Nielsen H. “Assessing the accuracy of prediction algorithms for classification: An overview”. Bioinformatics, 16(5), 412-424, 2000.
  • Freeman EA, Moisen GG. “A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa”. Ecological Modelling, 217(1-2), 48-58, 2008.
  • 2D Semantic Labeling Contest” “Online”. http://www2.isprs.org/commissions/comm3/wg4/semantic-labeling.html (2018).
  • Gerke M. “Use of the Stair Vision Library within the ISPRS 2D Semantic Labeling Benchmark (Vaihingen)”. Department of Earth Observation Science, University of Twente, Enschede, Netherlands, Technical Report, 2015.
  • Rottensteiner F, Sohn G, Gerke M, Baillard C, Benitez S, Breitkopf U. “ISPRS Test Project on Urban Classification and 3D Building Reconstruction”. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, I-3, 293-298, 2012.
There are 16 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Ali Sağlam

Nurdan Akhan Baykan

Publication Date October 21, 2019
Published in Issue Year 2019 Volume: 25 Issue: 5

Cite

APA Sağlam, A., & Akhan Baykan, N. (2019). Continuous time threshold selection for binary classification on polarized data. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 25(5), 596-602.
AMA Sağlam A, Akhan Baykan N. Continuous time threshold selection for binary classification on polarized data. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. October 2019;25(5):596-602.
Chicago Sağlam, Ali, and Nurdan Akhan Baykan. “Continuous Time Threshold Selection for Binary Classification on Polarized Data”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 25, no. 5 (October 2019): 596-602.
EndNote Sağlam A, Akhan Baykan N (October 1, 2019) Continuous time threshold selection for binary classification on polarized data. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 25 5 596–602.
IEEE A. Sağlam and N. Akhan Baykan, “Continuous time threshold selection for binary classification on polarized data”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 25, no. 5, pp. 596–602, 2019.
ISNAD Sağlam, Ali - Akhan Baykan, Nurdan. “Continuous Time Threshold Selection for Binary Classification on Polarized Data”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 25/5 (October 2019), 596-602.
JAMA Sağlam A, Akhan Baykan N. Continuous time threshold selection for binary classification on polarized data. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2019;25:596–602.
MLA Sağlam, Ali and Nurdan Akhan Baykan. “Continuous Time Threshold Selection for Binary Classification on Polarized Data”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 25, no. 5, 2019, pp. 596-02.
Vancouver Sağlam A, Akhan Baykan N. Continuous time threshold selection for binary classification on polarized data. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2019;25(5):596-602.

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