UCSY's Research Repository

Browsing International Conference on Computer Applications (ICCA) by Subject "Feature Selection"

Browsing International Conference on Computer Applications (ICCA) by Subject "Feature Selection"

Sort by: Order: Results:

  • Cloud Infrastructure Resource Demand Prediction Model Using Parameter Optimization and Feature Selection 
    Myo, Myint Myat; Thein, Thandar (Tenth International Conference On Computer Applications (ICCA 2012), 2012-02-28)
    Cloud computing offer highly scalable, and economical infrastructure for promising heterogeneous platforms and various applications. According to the growing demand nature of cloud infrastructure resources, the cloud ...
  • Feature Selection for Anomaly-Based Intrusion Detection System Using Information Gain and Mutual Correlation 
    Hlaing, Thuzar; Khine, May Aye (Ninth International Conference On Computer Applications (ICCA 2011), 2011-05-05)
    To avoid high computational costs in identifying intrusions by IDSs, the size of a dataset needs to be reduced. Feature selection is considered a problem of global combinatorial optimization in machine learning, which ...
  • A Framework for Intrusion Detection System Using Random Forests and Support Vector Machine 
    Oo, May Mar; Yi, Aye Mon (Tenth International Conference On Computer Applications (ICCA 2012), 2012-02-28)
    Due to continuous growth of the Internet technology, it needs to establish security mechanism. However, many current intrusion detection systems (IDSs) are rule-based systems, which have limitations to detect novel ...
  • Modified-MCA Based Feature Selection Model for Classification 
    Khaing, Myo; Kham, Nang Saing Moon (Ninth International Conference On Computer Applications (ICCA 2011), 2011-05-05)
    A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. A good feature set that contains highly correlated features ...
  • Reducing the Size of Feature Set by Using Modified MCA 
    Khaing, Myo (Fourteenth International Conference On Computer Applications (ICCA 2016), 2016-02-25)
    Reducing the size of a feature set, without altering the original representation, is an essential data processing step prior to applying a learning algorithm. The removal of irrelevant and redundant information improves ...

Search Repository



Browse

My Account