Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2015, Cilt: 36 Sayı: 3, 1466 - 1472, 13.05.2015

Öz

Kaynakça

  • Cover, T.M., Hart, P.E., Nearest neighbor pattern classification, Transactions on information theory, (1967), pp 21-27
  • Fix, E., Hodges, J.L., Discriminatory analysis nonparametric discrimination: small sample performance, project 21-49-004, (1952)
  • Fukunaga, K., Mantock, J.M., Nonparametric data reduction, transactions on pattern analysis and machine intelligence, (1984), pp 115-118
  • Joswik, A., A learning scheme for a fuzzy k-NN rule, pattern recognition letters, (1983), pp 287-289
  • Joswik, A., Roli, F., Dambra, C., A multistage synthesis of modified NN rule and its application for remote sensing images, prot IPTA (1993) pp 435-438
  • Serpico, S., Joswik, A., Roli, F., A parallel network of modified 1-NN and k-NN classifiers: application to remote-sensing image classification, Pattern recognition letters, (1998), pp 57-62
  • Robert A. Schowengerdt, 2007, Remote sensing Models and methods for image processing, Third Edition, pp558

Combining modified Nearest Neighborhood and fuzzy K-Nearest Neighborhood methods for classification of urban areas (Case study: Shahriar, Tehran, Iran)

Yıl 2015, Cilt: 36 Sayı: 3, 1466 - 1472, 13.05.2015

Öz

In this paper a method for combining classification of NN[1] and fuzzy k-NN[2]is represented. At first for reducing computation costs and noise elimination, some parts of training data which have the least value of dependency function, have been omitted. For classifying with modified NN method, an area is defined for each class, so that it comprises the entire training data belong to that class and the minimum possible amount of other classes. Pixels which have been in a defined area of one class would be labeled in that class and also pixels which have been in a defined area of more than one class would be labeled with fuzzy k-NN method. The main purpose of this task is to reduce computation costs compared to k-NN method and also to increase precision compared to NN method. This method (combining modified NN and fuzzy k-NN) is evaluated by data with 5 classes and it is been observed that computation time and classification precision have been improved by 14% and 2% respectively.

 

Kaynakça

  • Cover, T.M., Hart, P.E., Nearest neighbor pattern classification, Transactions on information theory, (1967), pp 21-27
  • Fix, E., Hodges, J.L., Discriminatory analysis nonparametric discrimination: small sample performance, project 21-49-004, (1952)
  • Fukunaga, K., Mantock, J.M., Nonparametric data reduction, transactions on pattern analysis and machine intelligence, (1984), pp 115-118
  • Joswik, A., A learning scheme for a fuzzy k-NN rule, pattern recognition letters, (1983), pp 287-289
  • Joswik, A., Roli, F., Dambra, C., A multistage synthesis of modified NN rule and its application for remote sensing images, prot IPTA (1993) pp 435-438
  • Serpico, S., Joswik, A., Roli, F., A parallel network of modified 1-NN and k-NN classifiers: application to remote-sensing image classification, Pattern recognition letters, (1998), pp 57-62
  • Robert A. Schowengerdt, 2007, Remote sensing Models and methods for image processing, Third Edition, pp558
Toplam 7 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Derleme
Yazarlar

Yaser Maghsoudı

Seyed Ali Vaez Mousavı Bu kişi benim

Yayımlanma Tarihi 13 Mayıs 2015
Yayımlandığı Sayı Yıl 2015 Cilt: 36 Sayı: 3

Kaynak Göster

APA Maghsoudı, Y., & Vaez Mousavı, S. A. (2015). Combining modified Nearest Neighborhood and fuzzy K-Nearest Neighborhood methods for classification of urban areas (Case study: Shahriar, Tehran, Iran). Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, 36(3), 1466-1472.
AMA Maghsoudı Y, Vaez Mousavı SA. Combining modified Nearest Neighborhood and fuzzy K-Nearest Neighborhood methods for classification of urban areas (Case study: Shahriar, Tehran, Iran). Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. Mayıs 2015;36(3):1466-1472.
Chicago Maghsoudı, Yaser, ve Seyed Ali Vaez Mousavı. “Combining Modified Nearest Neighborhood and Fuzzy K-Nearest Neighborhood Methods for Classification of Urban Areas (Case Study: Shahriar, Tehran, Iran)”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 36, sy. 3 (Mayıs 2015): 1466-72.
EndNote Maghsoudı Y, Vaez Mousavı SA (01 Mayıs 2015) Combining modified Nearest Neighborhood and fuzzy K-Nearest Neighborhood methods for classification of urban areas (Case study: Shahriar, Tehran, Iran). Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 36 3 1466–1472.
IEEE Y. Maghsoudı ve S. A. Vaez Mousavı, “Combining modified Nearest Neighborhood and fuzzy K-Nearest Neighborhood methods for classification of urban areas (Case study: Shahriar, Tehran, Iran)”, Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, c. 36, sy. 3, ss. 1466–1472, 2015.
ISNAD Maghsoudı, Yaser - Vaez Mousavı, Seyed Ali. “Combining Modified Nearest Neighborhood and Fuzzy K-Nearest Neighborhood Methods for Classification of Urban Areas (Case Study: Shahriar, Tehran, Iran)”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 36/3 (Mayıs 2015), 1466-1472.
JAMA Maghsoudı Y, Vaez Mousavı SA. Combining modified Nearest Neighborhood and fuzzy K-Nearest Neighborhood methods for classification of urban areas (Case study: Shahriar, Tehran, Iran). Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. 2015;36:1466–1472.
MLA Maghsoudı, Yaser ve Seyed Ali Vaez Mousavı. “Combining Modified Nearest Neighborhood and Fuzzy K-Nearest Neighborhood Methods for Classification of Urban Areas (Case Study: Shahriar, Tehran, Iran)”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, c. 36, sy. 3, 2015, ss. 1466-72.
Vancouver Maghsoudı Y, Vaez Mousavı SA. Combining modified Nearest Neighborhood and fuzzy K-Nearest Neighborhood methods for classification of urban areas (Case study: Shahriar, Tehran, Iran). Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. 2015;36(3):1466-72.