An Instance Selection Algorithm Based On Reverse k Nearest Neighbor

Journal Title: INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY - Year 2013, Vol 10, Issue 7

Abstract

Classification is one of the most important data mining techniques. It belongs to supervised learning. The objective of classification is to assign class label to unlabelled data. As data is growing rapidly, handling it has become a major concern. So preprocessing should be done before classification and hence data reduction is essential. Data reduction is to extract a subset of features from a set of features of a data set. Data reduction helps in decreasing the storage requirement and increases the efficiency of classification. A way to measure data reduction is reduction rate. The main thing here is choosing representative samples to the final data set. There are many instance selection algorithms which are based on nearest neighbor decision rule (NN). These algorithms select samples on incremental strategy or decremental strategy. Both the incremental algorithms and decremental algorithms take much processing time as they iteratively scan the dataset. There is another instance selection algorithm, reverse nearest neighbor reduction (RNNR) based on the concept of reverse nearest neighbor (RNN). RNNR does not iteratively scan the data set. In this paper, we extend the RNN to RkNN and we use the concept of RNNR to RkNN. RkNN finds the query objects that has the query point as their k nearest-neighbors. Our approach utilizes the advantage of RNN and proposes to use the concept of RkNN. We have taken the dataset of theatres, hospitals and restaurants and extracted the sample set. Classification has been done the resultant sample data set. We observe two parameters here they are classification accuracy and reduction rate.

Authors and Affiliations

Y. Jagruthi, Dr. Y. Ramadevi, A. Sangeeta

Keywords

Related Articles

BASIC SPECIFICATION REGARDING THE WIND POWER SYSTEMS CONTROL

The paper proposes an original metod to control the wind power system at variable wind speed. In the case of those high power wind systems that presents large inertia moments due to the variable wind speed, the rotation...

Implementation of Dynamic Threshold Method for Human Motion Detection in Video surveillance application

 Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams int...

ALTERNATIVES OF PROVIDING IPTV USING IP MULTIMEDIA SUBSYSTEM

In this paper, we specify the realization of IPTV platform based on IMS. We provide an analysis of advantages and disadvantages of possible alternatives of providing IPTV over IMS. On the example, which is contained in t...

A Digital Watermarking Algorithm Based on Wavelet Packet Transform and RBF Neural Network

Digital water marking technique suffered some problem of geometrical and some other attack. The process of attack deformed the quality of digital image and violet the rule of copyright protection low. For the roughness o...

WEB E-PROCUREMENT APPLICATION: USING TECHNOLOGY ACCEPTANCE MODEL TO SUPPLIERS USE OF E-PROCUREMENT IN MALAYSIA

This paper is research aim to extension of the technology acceptance model (TAM)to web E-procurement system. Focusing on suppliers and the E-procurement system impact on their organization performance, this paper has thu...

Download PDF file
  • EP ID EP650260
  • DOI 10.24297/ijct.v10i7.3217
  • Views 97
  • Downloads 0

How To Cite

Y. Jagruthi, Dr. Y. Ramadevi, A. Sangeeta (2013). An Instance Selection Algorithm Based On Reverse k Nearest Neighbor. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 10(7), 1858-1861. https://europub.co.uk/articles/-A-650260