Boosting backpropagation algorithm by stimulus-sampling: Application in computer-aided medical diagnosis
| Autor: | Smaranda Belciug, Florin Gorunescu |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Boosting (machine learning)
Heartbeat Computer science Markov process Health Informatics 02 engineering and technology Machine learning computer.software_genre 03 medical and health sciences symbols.namesake 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering Humans Diagnosis Computer-Assisted Medical diagnosis Artificial neural network business.industry Perceptron Backpropagation Computer Science Applications 030220 oncology & carcinogenesis symbols 020201 artificial intelligence & image processing Neural Networks Computer Artificial intelligence business computer Algorithms Stimulus sampling |
| Zdroj: | Journal of Biomedical Informatics. 63:74-81 |
| ISSN: | 1532-0464 |
| DOI: | 10.1016/j.jbi.2016.08.004 |
| Popis: | Display Omitted A novel stimulus-sampling approach for boosting the BP algorithm is proposed.Five medical datasets (colon cancer, breast cancer, diabetes, thyroid, fetal heartbeat) were used for assessment.Statistical benchmark has revealed the effectiveness of the model.The boosting procedure is easy to understand and implement.The model is prone to easily adapt to different medical datasets. 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 networks 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. |
| Databáze: | OpenAIRE |
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