Ensemble learning, multiple classifier system

1. How many classifiers should be used in an ensemble?
2. How do you combine multiple classifiers?
3. What is multiple classifier system?
4. What is the way to ensemble multiple classification or regression?

How many classifiers should be used in an ensemble?

An ensemble classifier is composed of 10 classifiers. One classifier is has an accuracy of 100% of the time in data subset X, and 0% all other times.

How do you combine multiple classifiers?

The simplest way of combining classifier output is to allow each classifier to make its own prediction and then choose the plurality prediction as the “final” output. This simple voting scheme is easy to implement and easy to understand, but it does not always produce the best possible results.

What is multiple classifier system?

Ensemble learning systems are also called multiple classifier systems. Ensemble algorithms yield better results if there are significant differences or diversity among the models. For example, more random decision trees lead to a stronger ensemble than entropy-reducing decision trees.

What is the way to ensemble multiple classification or regression?

Voting and averaging are two of the easiest ensemble methods. They are both easy to understand and implement. Voting is used for classification and averaging is used for regression. In both methods, the first step is to create multiple classification/regression models using some training dataset.