Dynamic Classifier Selection (DCS) involves multiple machine learning models to solve a classification problem. We provide a list of models that are trained with the training data. When we provide new data on which prediction needs to be made, the Dynamic Classifier Selection algorithm selects the model that can make the best prediction.

Now, in DCS, we provide a list of models, and the models are trained with the training data. When a prediction needs to be made on new data, we can take the following two approaches to solve the classification problem:

• We can use Overall Local Accuracy (OLA)
• We can use Local Class Accuracy (LCA)

In Overall Local Accuracy (OLA), the DCS algorithm evaluates all the models from the list of provided models. When new data comes, the DCS algorithm selects k nearest neighbors from the training data. Each previously trained model is then evaluated on the k neighbors. The model that performs the best is selected to make the prediction.

Dynamic Classifier Selection (DCS) with Overall Local Accuracy (OLA) using Python

We can use the following Python code to implement Dynamic Classifier Selection (DCS) with Overall Local Accuracy (OLA). …