title: Boosting backpropagation algorithm by stimulus-sampling: Application in computer-aided medical diagnosis.
abstract: Neural networks (NNs), in general, and multi-layer perceptron (MLP), in particular, represent one of the most efficient classifiers among the machine learning (ML) algorithms. Inspired by the stimulus-sampling paradigm, it is plausible to assume that the association of stimuli with the neurons in the output layer of a MLP can increase its performance. The stimulus-sampling process is assumed memoryless (Markovian), in the sense that the choice of a particular stimulus at a certain step, conditioned by the whole prior evolution of the learning process, depends only on the network's answer at the previous step. This paper proposes a novel learning technique, by enhancing the standard backpropagation algorithm performance with the aid of a stimulus-sampling procedure applied to the output neurons. The network uses the observable behavior that varies throughout the training process by stimulating the correct answers through corresponding rewards/penalties assigned to the output neurons. The proposed model has been applied in computer-aided medical diagnosis using five real-life breast cancer, colon cancer, diabetes, thyroid, and fetal heartbeat databases. The statistical comparison to well-established ML algorithms proved beyond doubt its efficiency and robustness.
authors: Department of Biostatistics and Informatics, University of Medicine and Pharmacy of Craiova, Craiova 200349, Romania. Electronic address: gorunef@gmail.com.
authors: Florin
authors: F
authors: Gorunescu
authors: Department of Computer Science, University of Craiova, Craiova 200585, Romania. Electronic address: smaranda.belciug@inf.ucv.ro.
authors: Smaranda
authors: S
authors: Belciug
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D016571:Neural Networks, Computer
keywords: Backpropagation/stimulus-sampling model; Breast cancer; Colon cancer; Diabetes; Fetal heartbeat; Thyroid