Items filtered by date: November 2013

Hybrid back-propagation training with evolutionary strategies

Published in Journal Articles

Abstract

This work presents a hybrid algorithm for neural network training that combines the back-propagation (BP) method with an evolutionary algorithm. In the proposed approach, BP updates the network connection weights, and a ( 1+1 ) Evolutionary Strategy (ES) adaptively modifies the main learning parameters. The algorithm can incorporate different BP variants, such as gradient descent with adaptive learning rate (GDA), in which case the learning rate is dynamically adjusted by the stochastic ( 1+1 )-ES as well as the deterministic adaptive rules of GDA; a combined optimization strategy known as memetic search. The proposal is tested on three different domains, time series prediction, classification and biometric recognition, using several problem instances. Experimental results show that the hybrid algorithm can substantially improve upon the standard BP methods. In conclusion, the proposed approach provides a simple extension to basic BP training that improves performance and lessens the need for parameter tuning in real-world problems.

  1. José Parra, Leonardo Trujillo and Patricia Melin. Hybrid back-propagation training with evolutionary strategies. Soft Computing, pages 1-12, 2013. URL, DOI BibTeX

    @article{,
    	year = 2013,
    	issn = "1432-7643",
    	journal = "Soft Computing",
    	doi = "10.1007/s00500-013-1166-8",
    	title = "Hybrid back-propagation training with evolutionary strategies",
    	url = "http://dx.doi.org/10.1007/s00500-013-1166-8",
    	publisher = "Springer Berlin Heidelberg",
    	keywords = "Neural networks; Back-propagation; Evolutionary strategies",
    	author = "Parra, José and Trujillo, Leonardo and Melin, Patricia",
    	pages = "1-12",
    	language = "English"
    }
    
Read more...

Identification of epilepsy stages from ECoG using genetic programming classifiers

Published in Journal Articles

Abstract

Objective: Epilepsy is a common neurological disorder, for which a great deal of research has been devoted to analyze and characterize brain activity during seizures. While this can be done by a human expert, automatic methods still lag behind. This paper analyzes neural activity captured with Electrocorticogram (ECoG), recorded through intracranial implants from Kindling model test subjects. The goal is to automatically identify the main seizure stages: Pre-Ictal, Ictal and Post-Ictal. While visually differentiating each stage can be done by an expert if the complete time-series is available, the goal here is to automatically identify the corresponding stage of short signal segments.

Methods and materials: The proposal is to pose the above task as a supervised classification problem and derive a mapping function that classifies each signal segment. Given the complexity of the signal patterns, it is difficult to a priori choose any particular classifier. Therefore, Genetic Programming (GP), a population based meta-heuristic for automatic program induction, is used to automatically search for the mapping functions. Two GP-based classifiers are used and extensively evaluated. The signals from epileptic seizures are obtained using the Kindling model of elicited epilepsy in rodent test subjects, for which a seizure was elicited and recorded on four separate days.

Results: Results show that signal segments from a single seizure can be used to derive accurate classifiers that generalize when tested on different signals from the same subject; i.e., GP can automatically produce accurate mapping functions for intra-subject classification. A large number of experiments are performed with the GP classifiers achieving good performance based on standard performance metrics. Moreover, a proof-of-concept real-world prototype is presented, where a GP classifier is transferred and hard-coded on an embedded system using a digital-to-analogue converter and a field programmable gate array, achieving a low average classification error of 14.55%, sensitivity values between 0.65 and 0.97, and specificity values between 0.86 and 0.94.

Conclusions: The proposed approach achieves good results for stage identification, particularly when compared with previous works that focus on this task. The results show that the problem of intra-class classification can be solved with a low error, and high sensitivity and specificity. Moreover, the limitations of the approach are identified and good operating configurations can be proposed based on the results.

  1. Arturo Sotelo, Enrique Guijarro, Leonardo Trujillo, Luis N Coria and Yuliana Martínez. Identification of epilepsy stages from ECoG using genetic programming classifiers. Computers in Biology and Medicine 43(11):1713 - 1723, 2013. URL, DOI BibTeX

    @article{,
    	title = "Identification of epilepsy stages from \{ECoG\} using genetic programming classifiers",
    	journal = "Computers in Biology and Medicine",
    	volume = 43,
    	number = 11,
    	pages = "1713 - 1723",
    	year = 2013,
    	doi = "http://dx.doi.org/10.1016/j.compbiomed.2013.08.016",
    	url = "http://www.sciencedirect.com/science/article/pii/S001048251300231X",
    	author = "Arturo Sotelo and Enrique Guijarro and Leonardo Trujillo and Luis N. Coria and Yuliana Mart\'{i}nez"
    }
    
Read more...
Subscribe to this RSS feed
Feedback