Iterative weighted k-NN for constructing missing feature values in Wisconsin breast cancer dataset

Muhammad Ashraf, Kim-Thang Le, Xu Huang

Research output: A Conference proceeding or a Chapter in BookConference contribution

3 Citations (Scopus)

Abstract

This paper presents a new approach for constructing missing feature values based on iterative nearest neighbors and distance metrics. The proposed approach employs weighted k nearest neighbors' algorithm and propagating the classification accuracy to a certain threshold. The proposed method showed improvement of classification accuracy of 0.005 in the constructed dataset than the original dataset which contain some missing feature values. The maximum classification accuracy was 0.9698 on k=1. This work is a component from a research for an automated diagnosing for breast cancer. The main aim of the current paper is to prepare the dataset for mining process. Future work includes applying the proposed method on more datasets.
Original languageEnglish
Title of host publication3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMiA), 2011
EditorsDr Hon Chi Tin, Dr Kae Dal Kwack, Dr Simon Fong
Place of PublicationMacau, China
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages23-27
Number of pages5
Volume1
ISBN (Electronic)9788988678497
ISBN (Print)9781467302319
Publication statusPublished - 2011
Event3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMiA), 2011 - Macau, Macau, China
Duration: 24 Oct 201126 Oct 2011

Conference

Conference3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMiA), 2011
Abbreviated titleICMIA 2011
CountryChina
CityMacau
Period24/10/1126/10/11

Cite this

Ashraf, M., Le, K-T., & Huang, X. (2011). Iterative weighted k-NN for constructing missing feature values in Wisconsin breast cancer dataset. In D. H. C. Tin, D. K. D. Kwack, & D. S. Fong (Eds.), 3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMiA), 2011 (Vol. 1, pp. 23-27). Macau, China: IEEE, Institute of Electrical and Electronics Engineers.