Graduation Year
2024
Document Type
Thesis
Degree
M.S.C.S.
Degree Name
MS in Computer Science (M.S.C.S.)
Degree Granting Department
Computer Science and Engineering
Major Professor
Srinivas Katkoori, Ph.D.
Committee Member
Shaun Canavan, Ph.D.
Committee Member
Sriram Chellappan, Ph.D.
Keywords
Feature Extraction, Hidden Layer, Hybrid Method, Neural Network Optimization, Neural Networks
Abstract
Identifying the ideal number of hidden neurons in multilayer perceptron (MLP) networks remains an ongoing challenge in the field of machine learning. Our research presents an innovative strategy to address this issue by combining data preprocessing, feature selection by a hybrid method, and a methodical approach to MLP architecture design. The extracted features through feature selection help to construct MLP architecture and implement k-fold cross-validation along with early stopping mechanism for finding optimal number of hidden neurons. Our method's key innovation is its capacity to dynamically adjust the hidden neuron count while optimizing model performance. Through iterative refinement of the hidden layer structure and continuous evaluation of performance metrics, we identify the most effective network configuration for classification tasks. To validate our approach, we conducted tests on seven distinct classification datasets from the UCI machine learning repository and Kaggle. Notably, we achieved 95.07% accuracy on the breast cancer dataset using only two hidden neurons.
Scholar Commons Citation
Boyidapu, Susmitha, "Optimizing Hidden Neurons in Multilayer Perceptrons with a Hybrid Feature Selection Approach for Classification Tasks" (2024). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/11172
