Ensemble Classifier Design Based on Random Forest Concept - Breast Cancer Diagnosis
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Resource Overview
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.
Detailed Documentation
The random forest mentioned in the text is an ensemble method that constructs a forest through randomized processes, containing multiple decision trees. Each decision tree in a random forest operates independently without inter-tree correlations. During implementation, the algorithm typically utilizes bootstrap sampling to create diverse training subsets for each tree while randomly selecting features at node splits. Once the forest is established, when a new input sample is introduced, every decision tree independently performs classification, determining the sample's predicted category. The final classification result is obtained by aggregating all tree predictions through majority voting—the class receiving the most votes becomes the ultimate prediction. This mechanism enables random forests to handle classification tasks effectively, providing reliable predictions with inherent resistance to overfitting through its ensemble structure and randomization techniques.
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