Event Tracking Algorithm Based on Negative-Example-Pruning Support Vector Machine
When training sets with uneven class sizes are used, the larger the sample size, the smaller the classification error of support vector machine (SVM), whereas the smaller the sample size, the larger the classification error. A negative-examples-pruning support vector machine (NEP-SVM) based algorithm for event tracking was proposed based on the analysis of the cause of this bias. The algorithm first pruned the negative examples, reserved and deleted a negative sample according to distance and its class label, then trained the new set with SVM to obtain a classifier and this algorithm compensates for the unfavorable impact caused by this bias. In addition, since posteriori probability of samples was important in improving the performance of event tracking, but traditional SVM did not provide posteriori probability, so the parameters of a sigmoid function were trained to map the SVM outputs into probabilities in this paper. Experimental results showed that the NEP-SVM is effective.