@article{MAKHILLAJIT20065105219,
    title = {A Hybrid Feature Selection Method  Using Modular  Perceptron  Networks},
    journal = {Asian Journal of Information Technology},
    volume = {5},
    number = {10},
    pages = {1088-1094},
    year = {2006},
    issn = {1682-3915},
    doi = {ajit.2006.1088.1094},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2006.1088.1094},
    author = {Yen-Po Lee,Wei-Yu Han,Wu-Ja Lin and},
    keywords = {Modular perception,hybrid feature,selection method},
    abstract = {In this study, we propose an efficient hybrid feature subset selection method to overcome the curse of dimensionality and to obtain good learning   performance on classification problems. The proposed method includes two steps: Scheming  an evaluation  function to create the rank of the feature significance, constructing  a new Binary Search Feature Subset (BSFS) algorithm to generate the optimum feature subset.  We  have  applied  the proposed method on a Modular Perceptron Network (MPN) to learn the realworld datasets.  It  shows  that  from  the experimental results the feature of the input data can be  decreased largely (less 75%~88%), the data presentations are reduced (less 67%~ 91%) and  a  small  size MPNs can be procured with learning and  testing performance maintained as  the good level  as  before.}
    }