Ensemble Classifier Design Based on Random Forest Concept - Breast Cancer Diagnosis

A random forest, as the name suggests, constructs a forest using random methods, comprising numerous decision trees where each tree operates independently without correlation. After the forest is built, when a new input sample arrives, each decision tree in the forest individually classifies the sample. The final prediction is determined by majority voting, selecting the class with the highest frequency among all trees. This ensemble approach enhances classification robustness and reduces overfitting through bootstrap aggregation (bagging) and random feature selection during tree construction.