Design of ANN-BP Classifier with Data Partitioning and Performance Evaluation

In this description, we elaborate on the design pipeline of the ANN-BP classifier and its application to datasets. Initially, we collect data and split it into two subsets: one half for training the classifier using the ANN-BP algorithm, and the other half for evaluating its performance. Next, we discuss the working principles of the BP (Backpropagation) algorithm, which is central to training the artificial neural network (ANN). Key steps include forward propagation to compute outputs and backward propagation to adjust weights based on error gradients. We then detail the classifier design process, covering the configuration of input and output layers, the selection of the number and size of hidden layers, and methods for initializing weights and biases—commonly implemented using functions like `initialize_weights()` with techniques such as Xavier or He initialization. Finally, we describe iterative training cycles, where the model undergoes multiple epochs to minimize loss functions (e.g., cross-entropy), followed by performance testing on the test set using metrics like accuracy, precision, and recall. Results are analyzed to interpret the classifier’s effectiveness and identify potential improvements.