Image Classification and Recognition Based on Deep Learning and Random Forest Algorithm
Algorithm 1
Input: sample set ; number of split attributes.
Step1: Select samples from the sample set using Bootstrap sampling.
Step2: Randomly select attributes and choose the best split attributes to build CART decision tree.
Step3: Repeat Step1 and Step2 for times to build CART decision trees.
Step4: Form a random forest with CART trees, for the test set decide which class the data belongs to by voting on the results, and the percentage that is different from the correct classification label is the classification error rate of RF.
