SVM Parameter Tuning with Grid Search and Its Impact on Reduction of Model Over-fitting
Petre Lameski, Eftim Zdravevski, Riste Mingov, Andrea Kulakov · Lecture notes in computer science · 2015
In this paper we describe our submission to the IJCRS’15 Data Mining Competition, which is concerned with prediction of dangerous concentrations of methane in longwalls of a Polish coalmine. We address the challenge of building robust classification models with support vector machines (SVMs) that are built from time series data. Moreover, we investigate the impact of parameter tuning of SVMs with grid search on the classification performance and its effect on preventing over-fitting. Our results show improvements of predictive performance with proper parameter tuning but also improved stability of the classification models even when the test data comes from a different time period and class distribution. By applying the proposed method we were able to build a classification model that predicts unseen test data even better than the training data, thus highlighting the non-over-fitting properties of the model. The submitted solution was about 2 % behind the winning solution.