**3. Background information**

### **3.1. Ensemble learning**

Ensemble learning is a machine learning technique where multiple learners are trained to solve the same problem and their predictions are combined with a single output that probably has better performance on average than any individual ensemble member. The fundamental idea behind ensemble learning is to combine weak learners into one, a strong learner, who has a better generalization error and is less sensitive to overfitting in the presence of noise or small sample size. This is because different classifiers can sometimes misclassify different patterns and accuracy can be improved by combining the decisions of complementary classifiers.

### **3.2. Elements of an ensemble classifier**

A typical ensemble framework for classification tasks contains four fundamental components descripted as follows:


and grading. While weighting methods are useful when combining classifiers built from a single learning algorithm and they have comparable success, meta-learning is a good choice for cases in which base classifiers consistently classify correctly or consistently misclassify.
