Intakosum, Sarun

Loading...
Thumbnail Image
Preferred name
Intakosum, Sarun
Main Affiliation
Email
sarun.in@kmitl.ac.th

Settings

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Borderline over-sampling in feature space for learning algorithms in imbalanced data environments
    (2016-01-01)
    Savetratanakaree, Kittipat
    ;
    Sookhanaphibarn, Kingkarn
    ;
    Intakosum, Sarun
    ;
    Thawonmas, Ruck
    In this paper, we propose a new approach to over-sample new minority-class instances along the borderline using the Euclidean distance in the feature space to improve support vector machine (SVM) performance in imbalanced data environments. SVM has been an outstandingly successful classifier in a wide variety of applications where balanced class data distribution is assumed. However, SVM is ineffective when coping with imbalanced datasets whereby the majorityclass instances far outnumber the minority-class instances. Our new approach, called Borderline Over-sampling in the Feature Space, can deal with imbalanced data to effectively recognize new minority-class instances for better classification with SVM. The results of our class prediction experiments using the proposed approach demonstrate better performance than the existing SMOTE, Borderline-SMOTE and borderline over-sampling methods in terms of the g-mean and F-measure.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Departure prediction of online game players
    (2014-01-01)
    Savetratanakaree, Kittipat
    ;
    Sookhanaphibarn, Kingkarn
    ;
    Intakosum, Sarun
    ;
    Thawonmas, Ruck
    ;
    Chen, Kuan Ta
    Most business models of online game companies usually depend on sale of virtual items and the monthly subscription fees. The prediction of player departure could increase revenues by giving special promotions out to the players who are expected to unsubscribe or quit playing the game. This paper proposes a departure prediction approach by using a new feature called "SLKdays" and a game revisitation. The feature "SLKdays" is defined as "Staytime", which is the time each player spending in an online game, of the last k days, and a game revisitation is the playing frequency in the last month to predict the next month subscription. We explore our new feature "SLKdays" to determine the optimal number of k for the departure prediction. With our proposed feature, the accuracy of the departure prediction is high, 91.92%, and the precision and recall rate are 98.22% and 84.51%. © (2014) Trans Tech Publications, Switzerland.