Ensemble Learning

Abstract

This chapter is concerned with the schemes of combining multiple machine learning algorithms with each other. We mention the four main combination schemes, depending on the role of the coordinator as the start of this chapter. Afterward, we describe the meta-learning for reinforcing the combination schemes. We present some partitions for implementing the ensemble learning. This chapter focuses on the combination of supervised learning algorithms for classifying data items. This chapter is intended to describe the ensemble learning, together with the meta-learning and the partition scheme.

In Sect. 13.1, we introduce the ensemble learning by providing its overviews and in Sect. 13.2, mention the schemes of combining machine learning algorithms. In Sect. 13.3, we devote to the meta-learning which is applicable to the coordinator of the machine learning algorithms. In Sect. 13.4, we mention the input partition, the architecture partition, and the training set partition which are necessary for the ensemble learning, and in Sect. 13.5, we make the summarization on this chapter and the further discussions.