CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A Study on Handling Non Linear Separation of Classes using Kernel based Supervised Noise Clustering Approach

by Ishuita SenGupta, Anil Kumar, Rakesh Kumar Dwivedi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 5
Year of Publication: 2018
Authors: Ishuita SenGupta, Anil Kumar, Rakesh Kumar Dwivedi
10.5120/ijca2018917552

Ishuita SenGupta, Anil Kumar, Rakesh Kumar Dwivedi . A Study on Handling Non Linear Separation of Classes using Kernel based Supervised Noise Clustering Approach. International Journal of Computer Applications. 181, 5 ( Jul 2018), 29-35. DOI=10.5120/ijca2018917552

@article{ 10.5120/ijca2018917552,
author = { Ishuita SenGupta, Anil Kumar, Rakesh Kumar Dwivedi },
title = { A Study on Handling Non Linear Separation of Classes using Kernel based Supervised Noise Clustering Approach },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2018 },
volume = { 181 },
number = { 5 },
month = { Jul },
year = { 2018 },
issn = { 0975-8887 },
pages = { 29-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number5/29714-2018917552/ },
doi = { 10.5120/ijca2018917552 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:05:07.448057+05:30
%A Ishuita SenGupta
%A Anil Kumar
%A Rakesh Kumar Dwivedi
%T A Study on Handling Non Linear Separation of Classes using Kernel based Supervised Noise Clustering Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 5
%P 29-35
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a framework of incorporating kernel methods with fuzzy based image classifiers. The goal of image classification is to separate images according to their visual content into two or more disjoint classes. The work demonstrates how non linearity among the different classes of remote sensing data with uncertainty are handled with Noise classifier without entropy(fuzzy classifier) using kernel approach for land use/land cover maps generation. It also show case the comparative study between performance of Noise Classifier with Euclidean Distance and Noise Classifier with Kernel functions. The introduction to Kernel function in fuzzy based classification techniques provides the basis for the development of more robust approaches to the classification problem.

References
  1. Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 100, 9–34. http://doi.org/10.1016/S0165-0114(99)80004-9
  2. Bezdek, J. C., Ehrlich, R., and Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences, 10(2–3), 191–203. http://doi.org/10.1016/0098-3004(84)90020-7
  3. Foody, G. M. (2000). Estimation of sub-pixel land cover composition in the presence of untrained classes. Computers and Geosciences, 26(4), 469–478. http://doi.org/10.1016/S0098-3004(99)00125-9 .
  4. Upadhyay, P., Ghosh, S. K., and Kumar, A. (2014). A Brief Review of Fuzzy Soft Classification and Assessment of Accuracy Methods for Identification of Single Land Cover. Studies in Surveying and Mapping Science (SSMS), 2(Mlc), 1–13.
  5. DAVÉ, R. & SEN, S. Noise clustering algorithm revisited. Fuzzy Information Processing Society, 1997. NAFIPS'97., 1997 Annual Meeting of the North American, 1997. IEEE, 199-204.
  6. Scholkopf, B., Burges, J. C., and Smola, A. J. (2008). Advances in Kernel Methods: Support Vector Learning, 373. http://doi.org/10.1016/j.neuropsychologia.2011.01.001.
  7. Dwivedi, R.K., Ghosh, S. K. and Kumar, A., 2013. Visualization of Uncertainty using entropy on Noise clustering with entropy classifier. 3rd IEEE International Advance Computing Conference (IACC-2013).
  8. Byju, A. P. (2015). Non-Linear Separation of classes using a Kernel based Fuzzy c -Means ( KFCM ) Approach. ITC, University of twente,The Netherlands.
  9. CHOTIWATTANA, W. Noise Clustering Algorithm based on Kernel Method. Advance Computing Conference, 2009. IACC 2009. IEEE International, 2009. IEEE, 56-60.
  10. Hofmann, T., Scholkopf, B., and Smola, A. J. (2008). Kernel methods in machine learning. The Annals of Statistics, 36(3), 1171–1220. http://doi.org/10.1214/009053607000000677
  11. Lin, H., and Lin, C. (n.d.). A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods, 1–32.
  12. Mittal, D., and Tripathy, B. K. (2015). Efficiency Analysis of Kernel Functions In Uncertainty Based C-Means Algorithms. 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 807–813.
  13. Binaghi, E., Brivio, P. a., Ghezzi, P., and Rampini, A. (1999). A fuzzy set-based accuracy assessment of soft classification. Pattern Recognition Letters, 20(9), 935–948. http://doi.org/10.1016/S0167-8655(99)00061-6
  14. USGS, Landsat Misson, Landsat8 Data Documentation and Information, https://landsat.usgs.gov/landsat-8
  15. Formosat-2 eoPortal Directory, Formosat-2 Overview and Specifications, https://earth.esa.int/web/eoportal/satellite-missions/f/formosat-2
Index Terms

Computer Science
Information Sciences

Keywords

Image Classification Fuzzy Classifier Kernel functions.

Powered by PhDFocusTM