Classification of EEG signals using a genetic-based machine learning classifier

This paper investigates the efficacy of the genetic-based learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and Fast Fourier Transform (FFT) methods were used to form feature vectors with which mental tasks can be discriminated. XCS achieved a maximum classification accuracy of 99.3% and a best average of 88.9%. The relative classification performance of XCS was then compared against four non-evolutionary classifier systems originating from different learning techniques. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices. © 2007 IEEE

Class decomposition for GA-based classifier agents – A Pitt approach

Incremental learning has been widely addressed in the machine learning literature to cope with learning tasks where the learning environment is ever changing or training samples become available over time. However, most research work explores incremental learning with statistical algorithms or neural networks, rather than evolutionary algorithms. The work in this paper employs genetic algorithms (GAs) as basic learning algorithms for incremental learning within one or more classifier agents in a multi-agent environment. Four new approaches with different initialization schemes are proposed. They keep the old solutions and use an “integration” operation to integrate them with new elements to accommodate new attributes, while biased mutation and crossover operations are adopted to further evolve a reinforced solution. The simulation results on benchmark classification data sets show that the proposed approaches can deal with the arrival of new input attributes and integrate them with the original input space. It is also shown that the proposed approaches can be successfully used for incremental learning and improve classification rates as compared to the retraining GA. Possible applications for continuous incremental training and feature selection are also discussed

A parallel and distributed genetic-based learning classifier system with application in human electroencephalographic signal classification

University of Technology, Sydney. Faculty of Engineering.Genetic-based Learning Classifier Systems have been proposed as a competent technology for the classification of medical data sets. What is not known about this class of system is twofold. Firstly, how does a Learning Classifier System (LCS) perform when applied to the single-step classification of multiple-channel, noisy, artefact-inclusive human EEG signals acquired from many participants? Secondly and more importantly, is how the learning classifier system performs when incorporated with migration strategies, inspired by multi- deme, coarse-grained Parallel Genetic Algorithms (PGA) to provide parallel and distributed classifier migration? This research investigates these open questions and concludes, subject to the considerations herein, that these technological approaches can provide competitive classification performance for such applications. We performed a preliminary examination and implementation of a parallel genetic algorithm and hybrid local search PGA using experimental methods. The parallelisation and incorporation of classical local search methods into a genetic algorithm are well known methods for increasing performance and we examine this. Furthermore, inspired by the significant improvements in convergence velocity and solution quality provided by the multi- deme, coarse-grained Parallel Genetic Algorithm, we incorporate the method into a learning classifier system with the aim of providing parallel and distributed classifier migration. As a result, a unique learning classifier system (pXCS) is proposed that improves classification accuracy, achieves increased learning rates and significantly reduces the classifier population during learning. It is compared to the extended learning Classifier System (XCS) and several state of the art non-evolutionary classifiers in the single-step classification of noisy, artefact- inclusive human EEG signals, derived from mental task experiments conducted using ten human participants. We also conclude that establishing an appropriate migration strategy is an important cause of pXCS learning and classification performance. However, an inappropriate migration rate, frequency or selection:replacement scheme can reduce performance and we document the factors associated with this. Furthermore, we conclude that both EEG segment size and representation both have a significant influence on classification performance. In effect, determining an appropriate representation of the raw EEG signal is tantamount to the classification method itself. This research allows us to further explore and incorporate pXCS evolved classifiers derived from multi-channel human EEG signals as an interface in the control of a device such as a powered wheelchair or brain-computer interface (BCI) applications

Gene Polymorphisms of the Renin-Angiotensin System and Bleeding Complications of Warfarin: Genetic-Based Machine Learning Models

Background: This study aimed to investigate the effects of genetic variants and haplotypes in the renin-angiotensin system (RAS) on the risk of warfarin-induced bleeding complications at therapeutic international normalized ratios (INRs). Methods: Four single nucleotide polymorphisms (SNPs) of AGT, two SNPs of REN, three SNPs of ACE, four SNPs of AGTR1, and one SNP of AGTR2, in addition to VKORC1 and CYP2C9 variants, were investigated. We utilized logistic regression and several machine learning methods for bleeding prediction. Results: The study included 142 patients, among whom 21 experienced bleeding complications. We identified a haplotype, H2 (TCG), carrying three SNPs of ACE (rs1800764, rs4341, and rs4353), which showed a significant relation with bleeding complications. After adjusting covariates, patients with H2/H2 experienced a 0.12-fold (95% CI 0.02-0.99) higher risk of bleeding complications than the others. In addition, G allele carriers of AGT rs5050 and A allele carriers of AGTR1 rs2640543 had 5.0- (95% CI 1.8-14.1) and 3.2-fold (95% CI 1.1-8.9) increased risk of bleeding complications compared with the TT genotype and GG genotype carriers, respectively. The AUROC values (mean, 95% CI) across 10 random iterations using five-fold cross-validated multivariate logistic regression, elastic net, random forest, support vector machine (SVM)-linear kernel, and SVM-radial kernel models were 0.732 (0.694-0.771), 0.741 (0.612-0.870), 0.723 (0.589-0.857), 0.673 (0.517-0.828), and 0.680 (0.528-0.832), respectively. The highest quartile group (≥75th percentile) of weighted risk score had approximately 12.0 times (95% CI 3.1-46.7) increased risk of bleeding, compared to the 25-75th percentile group, respectively. Conclusion: This study demonstrated that RAS-related polymorphisms, including the H2 haplotype of the ACE gene, could affect bleeding complications during warfarin treatment for patients with mechanical heart valves. Our results could be used to develop individually tailored intervention strategies to prevent warfarin-induced bleeding.ope

ATNoSFERES revisited

ATNoSFERES is a Pittsburgh style Learning Classifier System (LCS) in which the rules are represented as edges of an Augmented Transition Network. Genotypes are strings of tokens of a stack-based language, whose execution builds the labeled graph. The original ATNoSFERES, using a bitstring to represent the language tokens, has been favorably compared in previous work to several Michigan style LCSs architectures in the context of Non Markov problems. Several modifications of ATNoSFERES are proposed here: the most important one conceptually being a representational change: each token is now represented by an integer, hence the genotype is a string of integers; several other modifications of the underlying grammar language are also proposed. The resulting ATNoSFERES-II is validated on several standard animat Non Markov problems, on which it outperforms all previously published results in the LCS literature. The reasons for these improvement are carefully analyzed, and some assumptions are proposed on the underlying mechanisms in order to explain these good results

Machine Learning Approach for Optimizing Negotiation Agents

The increasing popularity of Internet and World Wide Web (WWW) fuels the rise of electronic commerce (E-Commerce). Negotiation plays an important role in ecommerce as business deals are often made through some kind of negotiations. Negotiation is the process of resolving conflicts among parties having different criteria so that they can reach an agreement in which all their constraints are satisfied. Automating negotiation can save human’s time and effort to solve these combinatorial problems. Intelligent Trading Agency (ITA) is an automated agentbased one-to-many negotiation framework which is incorporated by several one-toone negotiations. ITA uses constraint satisfaction approach to evaluate and generate offers during the negotiation. This one-to-many negotiation model in e-commerce retail has advantages in terms of customizability, scalability, reusability and robustness. Since negotiation agents practice predefined negotiation strategies, decisions of the agents to select the best course of action do not take the dynamics of negotiation into consideration. The lack of knowledge capturing between agents during the negotiation causes the inefficiency of negotiation while the final outcomes obtained are probably sub-optimal. The objective of this research is to implement machine learning approach that allows agents to reuse their negotiation experience to improve the final outcomes of one-to-many negotiation. The preliminary research on automated negotiation agents utilizes case-based reasoning, Bayesian learning and evolutionary approach to learn the negotiation. The geneticbased and Bayesian learning model of multi-attribute one-to-many negotiation, namely GA Improved-ITA and Bayes Improved-ITA are proposed. In these models, agents learn the negotiation by capturing their opponent’s preferences and constraints. The two models are tested in randomly generated negotiation problems to observe their performance in negotiation learning. The learnability of GA Improved-ITA enables the agents to identify their opponent’s preferable negotiation issues. Bayes Improved-ITA agents model their opponent’s utility structure by employing Bayesian belief updating process. Results from the experimental work indicate that it is promising to employ machine learning approach in negotiation problems. GA Improved-ITA and Bayes Improved-ITA have achieved better performance in terms of negotiation payoff, negotiation cost and justification of negotiation decision in comparison with ITA. The joint utility of GA Improved-ITA and Bayes Improved-ITA is 137.5% and 125% higher than the joint utility of ITA while the negotiation cost of GA Improved-ITA is 28.6% lower than ITA. The negotiation successful rate of GA Improved-ITA and Bayes Improved-ITA is 10.2% and 37.12% higher than ITA. By having knowledge of opponent’s preferences and constraints, negotiation agents can obtain more optimal outcomes. As a conclusion, the adaptive nature of agents will increase the fitness of autonomous agents in the dynamic electronic market rather than practicing the sophisticated negotiation strategies. As future work, the GA and Bayes Improved-ITA can be integrated with grid concept to allocate and acquire resource among cross-platform agents during negotiation

EECluster: An Energy-Efficient Tool for managing HPC Clusters

High Performance Computing clusters have become a very important element in research, academic and industrial communities because they are an excellent platform for solving a wide range of problems through parallel and distributed applications. Nevertheless, this high performance comes at the price of consuming large amounts of energy, which combined with notably increasing electricity prices are having an important economical impact, driving up power and cooling costs and forcing IT companies to reduce operation costs. To reduce the high energy consumptions of HPC clusters we propose a tool, named EECluster, for managing the energy-efficient allocation of the cluster resources, that works with both OGE/SGE and PBS/TORQUE Resource Management Systems (RMS) and whose decision-making mechanism is tuned automatically in a machine learning approach. Experimental studies have been made using actual workloads from the Scientific Modelling Cluster at Oviedo University and the academic-cluster used by the Oviedo University for teaching high performance computing subjects to evaluate the results obtained with the adoption of this too

EECluster: An Energy-Efficient Tool for managing HPC Clusters

High Performance Computing clusters have become a very important element in research, academic and industrial communities because they are an excellent platform for solving a wide range of problems through parallel and distributed applications. Nevertheless, this high performance comes at the price of consuming large amounts of energy, which combined with notably increasing electricity prices are having an important economical impact, driving up power and cooling costs and forcing IT companies to reduce operation costs. To reduce the high energy consumptions of HPC clusters we propose a tool, named EECluster, for managing the energy-efficient allocation of the cluster resources, that works with both OGE/SGE and PBS/TORQUE Resource Management Systems (RMS) and whose decision-making mechanism is tuned automatically in a machine learning approach. Experimental studies have been made using actual workloads from the Scientific Modelling Cluster at Oviedo University and the academic-cluster used by the Oviedo University for teaching high performance computing subjects to evaluate the results obtained with the adoption of this tool