Title :
Using Support Vector Machines for Numerical Prediction
Author :
Hussain, Shahid ; Khamisani, Vaqar
Author_Institution :
PAF-Karachi Inst. of Economic & Technol., Microsoft Pakistan, Karachi
Abstract :
We present a new methodology for prediction of numeric data using only classifiers for discrete data such as C4.5/J4.8 and/or support vector machine (SVM). We have used the "one-vs-all" approach to discretize the numeric data into binary and then used support vector machine (SVM) with boosting using free Weka toolkit. Empirical study was carried on real datasets taken from University of California, Irvine (UCI) machine learning repository.
Keywords :
mathematics computing; support vector machines; Weka toolkit; numerical prediction; one-vs-all approach; support vector machines; Boosting; Decision trees; Equations; Machine learning; Predictive models; Regression analysis; Regression tree analysis; Support vector machine classification; Support vector machines; Testing; Support vector machine; WEKA; boosting; classification; numerical prediction;
Conference_Titel :
Multitopic Conference, 2007. INMIC 2007. IEEE International
Conference_Location :
Lahore
Print_ISBN :
978-1-4244-1552-6
Electronic_ISBN :
978-1-4244-1553-3
DOI :
10.1109/INMIC.2007.4557695
