Using Parallel Hyperplanes to Partition Training Set for Ensemble Learning
Aiming to handle the problem which generalization ability is decreased by partitioning training set,a machine learning algorithm was proposed to combine classifiers which are trained on training set partitioned by parallel hyperplanes.It used many clusters of parallel hyperplanes to partition training set on which each base classifier was trained by a SVM modular network algorithm and all these base classifiers were combined by majority voting strategy when testing.The experimental results on 3 large scale classification problems illustrate that ensemble learning can effectively reduce variance while keep bias and so cut down the descent of generalization ability but does not increase the training and test time.