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.
    Ashraf, Muhammad ; Le, Kim-Thang ; Huang, Xu. / Iterative weighted k-NN for constructing missing feature values in Wisconsin breast cancer dataset. 3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMiA), 2011. editor / Dr Hon Chi Tin ; Dr Kae Dal Kwack ; Dr Simon Fong. Vol. 1 Macau, China : IEEE, Institute of Electrical and Electronics Engineers, 2011. pp. 23-27
    @inproceedings{1084255b63c848ab8cee2640697abc0f,
    title = "Iterative weighted k-NN for constructing missing feature values in Wisconsin breast cancer dataset",
    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.",
    keywords = "Data Mining, Breast Cancer, Feature Values",
    author = "Muhammad Ashraf and Kim-Thang Le and Xu Huang",
    year = "2011",
    language = "English",
    isbn = "9781467302319",
    volume = "1",
    pages = "23--27",
    editor = "Tin, {Dr Hon Chi} and Kwack, {Dr Kae Dal} and Fong, {Dr Simon}",
    booktitle = "3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMiA), 2011",
    publisher = "IEEE, Institute of Electrical and Electronics Engineers",
    address = "United States",

    }

    Ashraf, M, Le, K-T & Huang, X 2011, Iterative weighted k-NN for constructing missing feature values in Wisconsin breast cancer dataset. in DHC Tin, DKD Kwack & DS Fong (eds), 3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMiA), 2011. vol. 1, IEEE, Institute of Electrical and Electronics Engineers, Macau, China, pp. 23-27, 3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMiA), 2011, Macau, China, 24/10/11.

    Iterative weighted k-NN for constructing missing feature values in Wisconsin breast cancer dataset. / Ashraf, Muhammad; Le, Kim-Thang; Huang, Xu.

    3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMiA), 2011. ed. / Dr Hon Chi Tin; Dr Kae Dal Kwack; Dr Simon Fong. Vol. 1 Macau, China : IEEE, Institute of Electrical and Electronics Engineers, 2011. p. 23-27.

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

    TY - GEN

    T1 - Iterative weighted k-NN for constructing missing feature values in Wisconsin breast cancer dataset

    AU - Ashraf, Muhammad

    AU - Le, Kim-Thang

    AU - Huang, Xu

    PY - 2011

    Y1 - 2011

    N2 - 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.

    AB - 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.

    KW - Data Mining

    KW - Breast Cancer

    KW - Feature Values

    M3 - Conference contribution

    SN - 9781467302319

    VL - 1

    SP - 23

    EP - 27

    BT - 3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMiA), 2011

    A2 - Tin, Dr Hon Chi

    A2 - Kwack, Dr Kae Dal

    A2 - Fong, Dr Simon

    PB - IEEE, Institute of Electrical and Electronics Engineers

    CY - Macau, China

    ER -

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