Improving SVM-Linear predictions using CART for example selection

Authors:
João M. Moreira;Alípio M. Jorge;Carlos Soares;Jorge Freire de Sousa
Affiliations:
Faculty of Engineering, University of Porto, Portugal;Faculty of Economics, LIACC, University of Porto, Portugal;Faculty of Economics, LIACC, University of Porto, Portugal;Faculty of Economics, LIACC, University of Porto, Portugal
Venue:
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Year:
2006

Citing 4
Cited 0

Selection of relevant features and examples in machine learning

Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
A study of support vectors on model independent example selection

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
On Issues of Instance Selection

Data Mining and Knowledge Discovery

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes the study on example selection in regression problems using μ-SVM (Support Vector Machine) linear as prediction algorithm. The motivation case is a study done on real data for a problem of bus trip time prediction. In this study we use three different training sets: all the examples, examples from past days similar to the day where prediction is needed, and examples selected by a CART regression tree. Then, we verify if the CART based example selection approach is appropriate on different regression data sets. The experimental results obtained are promising.