Dynamic Incremental SVM learning Algorithm for Mining Data Streams

Incremental SVM framework is often designed to deal with large-scale learning and classification problems. The paper presents a new dynamic incremental learning algorithm for mining data streams. The multiple classifiers are constructed according to the statistic characters of batched training data in data streams. The feature space of all data is partitioned according to the performance of each classifier and the statistical characters on each region are counted. The classifier that has the best performance on the region near the test data is selected as the final output. The experimental results confirm the feasibility and validity of the proposed algorithm.